bibliotheek.bib

@book{Cohen1988,
address = {Hilsdale},
author = {Cohen, J.},
publisher = {Lawrence Earlbaum Associates},
title = {{Statistical power analysis for the behavioral sciences}},
year = {1988}
}
@article{efron2007,
abstract = {Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothe-sis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations arise in proteomics, spec-troscopy, imaging, and social science surveys. This paper uses false discovery rate methods to carry out both size and power calculations on large-scale problems. A simple empirical Bayes approach allows the false discovery rate (fdr) analysis to proceed with a minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy formulas are derived for estimated false discovery rates, and used to compare different methodologies: local or tail-area fdr's, theoretical, permutation, or empirical null hypothesis estimates. Two microarray data sets as well as simulations are used to evaluate the methodology, the power diagnostics showing why nonnull cases might easily fail to appear on a list of " significant " discoveries.},
archivePrefix = {arXiv},
arxivId = {arXiv:0710.2245v1},
author = {Efron, Bradley},
doi = {10.1214/009053606000001460},
eprint = {arXiv:0710.2245v1},
file = {:Users/Joke/Downloads/efron.pdf:pdf},
isbn = {0090-5364},
issn = {00905364},
journal = {Annals of Statistics},
keywords = {Empirical Bayes,Empirical null,Large-scale simultaneous inference,Local false discovery rates},
number = {4},
pages = {1351--1377},
title = {{Size, power and false discovery rates}},
volume = {35},
year = {2007}
}
@incollection{Henson2007,
abstract = {This chapter begins with an overview of the various types of experimental design, before proceeding to vari- ous modelling choices, such as the use of events versus epochs. It then covers some practical issues concerning the effective temporal sampling of blood oxygenation- level-dependent (BOLD) responses and the problem of different slice acquisition times. The final and main part of the chapter concerns the statistical efficiency of functional magnetic resonance imaging (fMRI) designs, as a function of stimulus onset asynchrony (SOA) and the ordering of different stimulus-types. These consider- ations allow researchers to optimize the efficiency of their fMRI designs.},
author = {Henson, R.},
booktitle = {Statistical Parametric Mapping: The Analysis of Functional Brain Images},
doi = {10.1016/B978-012372560-8/50015-2},
file = {:Users/Joke/Documents/Onderzoek/ProjectsOngoing/DesignEfficiency/Literature/Henson{\_}SPM{\_}06{\_}preprint.pdf:pdf},
isbn = {9780123725608},
issn = {10518215},
pages = {193--210},
pmid = {1000104770},
title = {{Efficient experimental design for fMRI}},
year = {2007}
}
@article{Zehetmayer2015,
author = {Zehetmayer, Sonja and Graf, Alexandra C. and Posch, Martin},
doi = {10.1515/sagmb-2014-0025},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zehetmayer, Graf, Posch - 2015 - Sample size reassessment for a two-stage design controlling the false discovery rate.pdf:pdf},
isbn = {1544-6115 (Electronic)
1544-6115 (Linking)},
issn = {1544-6115, 2194-6302},
journal = {Statistical Applications in Genetics and Molecular Biology},
keywords = {adaptive design,false discovery rate,high-dimensional data,two-stage design},
number = {5},
pages = {429--442},
pmid = {26461844},
title = {{Sample size reassessment for a two-stage design controlling the false discovery rate}},
url = {http://www.degruyter.com/view/j/sagmb.2015.14.issue-5/sagmb-2014-0025/sagmb-2014-0025.xml},
volume = {14},
year = {2015}
}
@article{Cheng2015,
archivePrefix = {arXiv},
arxivId = {1503.01328},
author = {Cheng, Dan and Schwartzman, Armin},
doi = {arXiv:1503.01328},
eprint = {1503.01328},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Cheng, Schwartzman - 2015 - On the explicit height distribution and expected number of local maxima of isotropic Gaussian Random Fiel(2).pdf:pdf},
issn = {1572915X},
journal = {biorXiv},
keywords = {Euler characteristic,Gaussian orthogonal ensemble,Height,Isotropic field,Local maxima,Overshoot,Riemannian manifold,Sphere},
title = {{On the explicit height distribution and expected number of local maxima of isotropic Gaussian Random Fields}},
year = {2015}
}
@article{Eklund2016,
archivePrefix = {arXiv},
arxivId = {1511.01863},
author = {Eklund, Anders and Nichols, Thomas E. and Knutsson, Hans},
doi = {10.1073/pnas.1602413113},
eprint = {1511.01863},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Eklund, Nichols, Knutsson - 2016 - Cluster failure Why fMRI inferences for spatial extent have inflated false-positive rates.pdf:pdf},
isbn = {0027-8424},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
pages = {201602413},
pmid = {27357684},
title = {{Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates}},
url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1602413113},
year = {2016}
}
@article{Skol2006,
abstract = {Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) {\textgreater}or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) {\textgreater}or= 0.01).},
author = {Skol, Andrew D and Scott, Laura J and Abecasis, Gon{\c{c}}alo R and Boehnke, Michael},
doi = {10.1038/ng1706},
file = {:Users/Joke/Downloads/ng1706.pdf:pdf},
isbn = {1061-4036 (Print)$\backslash$r1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nature genetics},
number = {2},
pages = {209--213},
pmid = {16415888},
title = {{Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.}},
volume = {38},
year = {2006}
}
@article{Hughett2007,
author = {Hughett, Paul},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hughett - 2007 - Accurate Computation of the F-to-z and t-to-z Transforms for Large Arguments.pdf:pdf},
isbn = {1548-7660},
issn = {1548-7660},
journal = {Journal of Statistical Software},
keywords = {a set of images,and mazziotta 1997,at each voxel of,dolan,evaluates some statistical hypothesis,f -to- z transform,floating-point precision,frackowiak,friston,frith,spm,statistical computation,statistical parametric mapping,t -to- z,transform,which have been registered},
number = {1},
pages = {1--5},
title = {{Accurate Computation of the F-to-z and t-to-z Transforms for Large Arguments}},
url = {http://www.jstatsoft.org/v23/c01},
volume = {23},
year = {2007}
}
@article{Byrd1995,
author = {Byrd, RH and Lu, P and Nocedal, J and Zhu, C},
journal = {SIAM Journal on scientific computing},
number = {5},
pages = {1190--1208},
title = {{A limited memory algorithm for bound constrained optimization}},
volume = {16},
year = {1995}
}
@article{Kiebel1999,
abstract = {The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i. e., P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e.g., the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.},
author = {Kiebel, S J and Poline, J B and Friston, K J and Holmes, a P and Worsley, K J},
doi = {10.1006/nimg.1999.0508},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Kiebel et al. - 1999 - Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear.pdf:pdf},
isbn = {1053-8119 (Print)$\backslash$r1053-8119 (Linking)},
issn = {1053-8119},
journal = {NeuroImage},
number = {6},
pages = {756--766},
pmid = {10600421},
title = {{Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model.}},
volume = {10},
year = {1999}
}
@misc{JohnWhitehead2001,
author = {{John Whitehead}, Susan Todd, Anne Whitehead, Nigel Stallard},
booktitle = {British Journal of Clinical Pharmacology},
language = {en},
month = {may},
number = {5},
pages = {393},
publisher = {Wiley-Blackwell},
title = {{Interim analyses in clinical trials}},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2014469/},
volume = {51},
year = {2001}
}
@misc{SPM,
address = {London, UK},
author = {Ashburner, J and Friston, Karl J and Penny, W. D.},
title = {{SPM}},
year = {2012}
}
@misc{R,
address = {Vienna, Austria},
author = {{R Core Team}},
publisher = {R foundation for statistical computing},
title = {{R: A language and environment for statistical computing}},
year = {2014}
}
@article{Kirk1996,
author = {Kirk, Roger E.},
journal = {Educational and psychological measurement},
pages = {746----759},
title = {{Practical significance: a concept whose time has come}},
volume = {56},
year = {1996}
}
@article{Thyreau2012,
abstract = {In this paper we investigate the use of classical fMRI Random Effect (RFX) group statistics when analyzing a very large cohort and the possible improvement brought from anatomical information. Using 1326 subjects from the IMAGEN study, we first give a global picture of the evolution of the group effect t-value from a simple face-watching contrast with increasing cohort size. We obtain a wide activated pattern, far from being limited to the reasonably expected brain areas, illustrating the difference between statistical significance and practical significance. This motivates us to inject tissue-probability information into the group estimation, we model the BOLD contrast using a matter-weighted mixture of Gaussians and compare it to the common, single-Gaussian model. In both cases, the model parameters are estimated per-voxel for one subgroup, and the likelihood of both models is computed on a second, separate subgroup to reflect model generalization capacity. Various group sizes are tested, and significance is asserted using a 10-fold cross-validation scheme. We conclude that adding matter information consistently improves the quantitative analysis of BOLD responses in some areas of the brain, particularly those where accurate inter-subject registration remains challenging.},
author = {Thyreau, Benjamin and Schwartz, Yannick and Thirion, Bertrand and Frouin, Vincent and Loth, Eva and Vollst{\"{a}}dt-Klein, Sabine and Paus, Tomas and Artiges, Eric and Conrod, Patricia J and Schumann, Gunter and Whelan, Robert and Poline, Jean-Baptiste},
doi = {10.1016/j.neuroimage.2012.02.083},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Thyreau et al. - 2012 - Very large fMRI study using the IMAGEN database sensitivity-specificity and population effect modeling in relati.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Databases, Factual,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Models, Neurological,Models, Statistical,Normal Distribution,Oxygen,Oxygen: blood,Population,Reproducibility of Results},
month = {may},
number = {1},
pages = {295--303},
pmid = {22425669},
publisher = {Elsevier Inc.},
title = {{Very large fMRI study using the IMAGEN database: sensitivity-specificity and population effect modeling in relation to the underlying anatomy.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22425669},
volume = {61},
year = {2012}
}
@article{Poldrack2012,
author = {Poldrack, RA},
doi = {10.1016/j.neuroimage.2011.08.007.The},
file = {:Users/Joke/Downloads/nihms609319.pdf:pdf},
journal = {Neuroimage},
keywords = {cognitive neuroscience,connectivity,fmri,functional magnetic resonance imaging,i was unaware of,in 1989,it may come as,meta-analysis,no,ongoing race to develop,ontology,school for cognitive psychology,the,when i began graduate},
number = {2},
pages = {1216--1220},
title = {{The future of fMRI in cognitive neuroscience}},
url = {http://www.sciencedirect.com/science/article/pii/S1053811911008949},
volume = {62},
year = {2012}
}
@article{Poldrack2013,
abstract = {The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.},
author = {Poldrack, Russell a and Barch, Deanna M and Mitchell, Jason P and Wager, Tor D and Wagner, Anthony D and Devlin, Joseph T and Cumba, Chad and Koyejo, Oluwasanmi and Milham, Michael P},
doi = {10.3389/fninf.2013.00012},
file = {:Users/Joke/Downloads/fninf-07-00012.pdf:pdf},
issn = {1662-5196},
journal = {Frontiers in neuroinformatics},
keywords = {classification,data sharing,informatics,informatics, data sharing, metadata, multivariate,,metadata,multivariate},
month = {jan},
number = {July},
pages = {12},
pmid = {23847528},
title = {{Toward open sharing of task-based fMRI data: the OpenfMRI project.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3703526{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2013}
}
@article{Wager2007a,
abstract = {Meta-analysis is an increasingly popular and valuable tool for summarizing results across many neuroimaging studies. It can be used to establish consensus on the locations of functional regions, test hypotheses developed from patient and animal studies and develop new hypotheses on structure-function correspondence. It is particularly valuable in neuroimaging because most studies do not adequately correct for multiple comparisons; based on statistical thresholds used, we estimate that roughly 10-20{\%} of reported activations in published studies are false positives. In this article, we briefly summarize some of the most popular meta-analytic approaches and their limitations, and we outline a revised multilevel approach with increased validity for establishing consistency across studies. We also discuss multivariate methods by which meta-analysis can be used to develop and test hypotheses about co-activity of brain regions. Finally, we argue that meta-analyses can make a uniquely valuable contribution to predicting psychological states from patterns of brain activity, and we briefly discuss some methods for making such predictions.},
author = {Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},
doi = {10.1093/scan/nsm015},
file = {:Users/Joke/Downloads/150.full.pdf:pdf},
issn = {1749-5024},
journal = {Social cognitive and affective neuroscience},
keywords = {Forecasting,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: trends},
month = {jun},
number = {2},
pages = {150--8},
pmid = {18985131},
title = {{Meta-analysis of functional neuroimaging data: current and future directions.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2555451{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {2},
year = {2007}
}
@article{Bennett1995,
author = {Bennett, Craig M and Baird, Abigail A and Miller, Michael B and Wolford, George L},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bennett et al. - 1995 - Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon An argument for multipl.pdf:pdf},
pages = {1995},
title = {{Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon : An argument for multiple comparisons correction}},
year = {1995}
}
@inproceedings{Bennett2009a,
author = {Bennett, Craig M and Baird, Abigail A and Miller, Michael B and Wolford, George L},
booktitle = {15th Annual meeting of the Organisation for Human Brain Mapping},
title = {{Neural correlates of interspecies perspective taking in the post-mortem atlantic salmon: an argument for proper multiple comparisons corrections.}},
year = {2009}
}
@article{Reiss2012,
author = {Reiss, Philip T and Schwartzman, Armin and Lu, Feihan and Huang, Lei and Proal, Erika},
doi = {10.1016/j.neuroimage.2012.07.040},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Reiss et al. - 2012 - Paradoxical results of adaptive false discovery rate procedures in neuroimaging studies.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adjusted p-values,Attention deficit/hyperactivity disorder,Cortical thickness,False discovery rate,Multiple testing,q-values},
number = {4},
pages = {1833--1840},
publisher = {Elsevier Inc.},
title = {{Paradoxical results of adaptive false discovery rate procedures in neuroimaging studies}},
url = {http://dx.doi.org/10.1016/j.neuroimage.2012.07.040},
volume = {63},
year = {2012}
}
@article{Friston2012,
abstract = {As an expert reviewer, it is sometimes necessary to ensure a paper is rejected. This can sometimes be achieved by highlighting improper statistical practice. This technical note provides guidance on how to critique the statistical analysis of neuroimaging studies to maximise the chance that the paper will be declined. We will review a series of critiques that can be applied universally to any neuroimaging paper and consider responses to potential rebuttals that reviewers might encounter from authors or editors.},
author = {Friston, Karl J},
doi = {10.1016/j.neuroimage.2012.04.018},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friston - 2012 - Ten ironic rules for non-statistical reviewers.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Neuroimaging,Peer Review,Research,Research Design,Research: methods,Statistics as Topic,Statistics as Topic: methods},
month = {jul},
number = {4},
pages = {1300--10},
pmid = {22521475},
publisher = {Elsevier Inc.},
title = {{Ten ironic rules for non-statistical reviewers.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22521475},
volume = {61},
year = {2012}
}
@article{Homola2012,
abstract = {Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke's understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships.},
author = {Homola, Gy{\"{o}}rgy a and Jbabdi, Saad and Beckmann, Christian F and Bartsch, Andreas Joachim},
doi = {10.1371/journal.pone.0049451},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Homola et al. - 2012 - A brain network processing the age of faces.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
keywords = {Adult,Age Factors,Brain,Brain Mapping,Brain Mapping: methods,Brain: pathology,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: methods,Face,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Pattern Recognition,Probability,Psychometrics,Sex Factors,Temporal Lobe,Temporal Lobe: pathology,Temporal Lobe: physiology,Visual,Visual: physiology},
month = {jan},
number = {11},
pages = {e49451},
pmid = {23185334},
title = {{A brain network processing the age of faces.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3502502{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2012}
}
@article{Wasserman2004,
author = {Wasserman, Larry and Genovese, Christopher},
doi = {10.1214/009053604000000283},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wasserman, Genovese - 2004 - A stochastic process approach to false discovery control.pdf:pdf},
issn = {0090-5364},
journal = {The Annals of Statistics},
keywords = {and phrases,false discovery rate,multiple testing,p -values},
month = {jun},
number = {3},
pages = {1035--1061},
title = {{A stochastic process approach to false discovery control}},
url = {http://projecteuclid.org/Dienst/getRecord?id=euclid.aos/1085408494/},
volume = {32},
year = {2004}
}
@book{Poldrack2011,
author = {Poldrack, Russell and Mumford, Jeanette A and Nichols, Thomas E},
title = {{Handbook of functional MRI data analysis}},
year = {2011}
}
@article{VanEssen2012,
abstract = {The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.},
author = {{Van Essen}, D C and Ugurbil, K and Auerbach, E and Barch, Deanna M and Behrens, T E J and Bucholz, R and Chang, A and Chen, L and Corbetta, M and Curtiss, S W and {Della Penna}, S and Feinberg, D and Glasser, M F and Harel, N and Heath, a C and Larson-Prior, L and Marcus, D and Michalareas, G and Moeller, S and Oostenveld, R and Petersen, S E and Prior, F and Schlaggar, B L and Smith, Stephen M and Snyder, a Z and Xu, J and Yacoub, E},
doi = {10.1016/j.neuroimage.2012.02.018},
file = {:Users/Joke/Downloads/1-s2.0-S1053811912001954-main.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Connectome,Connectome: methods,Humans},
month = {oct},
number = {4},
pages = {2222--31},
pmid = {22366334},
title = {{The Human Connectome Project: a data acquisition perspective.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3606888{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {62},
year = {2012}
}
@article{Jenkinson2012,
abstract = {FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.},
author = {Jenkinson, Mark and Beckmann, Christian F and Behrens, Timothy E J and Woolrich, Mark W and Smith, Stephen M},
doi = {10.1016/j.neuroimage.2011.09.015},
file = {:Users/Joke/Downloads/1-s2.0-S1053811911010603-main.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {20th Century,21st Century,Brain,Brain Mapping,Brain Mapping: history,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Computer-Assisted,Computer-Assisted: history,Computer-Assisted: methods,Diffusion Magnetic Resonance Imaging,Diffusion Magnetic Resonance Imaging: history,Diffusion Magnetic Resonance Imaging: methods,History,Humans,Image Processing,Software,Software: history},
month = {aug},
number = {2},
pages = {782--90},
pmid = {21979382},
title = {{Fsl.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21979382},
volume = {62},
year = {2012}
}
@article{Durnez2015,
author = {Durnez, Joke and Degryse, J. (Ghent University)},
journal = {R-News},
title = {{NeuroPower}},
year = {2015}
}
@article{Roels2014,
author = {Roels, S.P. and Bossier, H. and Loeys, T. and Moerkerke, B.},
doi = {10.1016/j.jneumeth.2014.10.024},
file = {:Users/Joke/Downloads/1-s2.0-S0165027014003926-main.pdf:pdf},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
month = {nov},
pages = {1--11},
publisher = {Elsevier B.V.},
title = {{Data-analytical stability of cluster-wise and peak-wise inference in fMRI data analysis}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0165027014003926},
year = {2014}
}
@article{Woo2014,
abstract = {Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p{\textless}.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p{\textless}.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods.},
author = {Woo, Choong-Wan and Krishnan, Anjali and Wager, Tor D},
doi = {10.1016/j.neuroimage.2013.12.058},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Woo, Krishnan, Wager - 2014 - Cluster-extent based thresholding in fMRI analyses pitfalls and recommendations.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Cluster-extent thresholding,FSL,False discovery rate,Family-wise error rate,Gaussian random fields,Multiple comparisons,Primary threshold,SPM,cluster-extent thresholding,fMRI,multiple comparisons},
month = {may},
pages = {412--9},
pmid = {24412399},
publisher = {Elsevier Inc.},
title = {{Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24412399},
volume = {91},
year = {2014}
}
@article{Kriegeskorte2010,
author = {Kriegeskorte, Nikolaus and Simmons, W Kyle and Bellgowan, Patrick S F and Baker, Chris I},
doi = {10.1038/nn.2303.Circular},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Kriegeskorte et al. - 2010 - Circular Analysis in systems neuroscience - the dangers of double dipping.pdf:pdf},
journal = {Nature Neuroscience},
number = {5},
pages = {535--540},
title = {{Circular Analysis in systems neuroscience - the dangers of double dipping}},
volume = {12},
year = {2010}
}
@article{Friston1999,
abstract = {This article considers the efficiency of event-related fMRI designs in terms of the optimum temporal pattern of stimulus or trial presentations. The distinction between "stochastic" and "deterministic" is used to distinguish between designs that are specified in terms of the probability that an event will occur at a series of time points (stochastic) and those in which events always occur at prespecified time (deterministic). Stochastic designs may be "stationary," in which the probability is constant, or nonstationary, in which the probabilities change with time. All these designs can be parameterized in terms of a vector of occurrence probabilities and a prototypic design matrix that embodies constraints (such as the minimum stimulus onset asynchrony) and the model of hemodynamic responses. A simple function of these parameters is presented and used to compare the relative efficiency of different designs. Designs with slow modulation of occurrence probabilities are generally more efficient than stationary designs. Interestingly the most efficient design is a conventional block design. A critical point, made in this article, is that the most efficient design for one effect may not be the most efficient for another. This is particularly important when considering evoked responses and the differences among responses. The most efficient designs for evoked responses, as opposed to differential responses, require trial-free periods during which baseline levels can be attained. In the context of stochastic, rapid-presentation designs this is equivalent to the inclusion of "null events."},
author = {Friston, Karl J and Zarahn, E and Josephs, O and Henson, R N and Dale, a M},
doi = {10.1006/nimg.1999.0498},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friston et al. - 1999 - Stochastic designs in event-related fMRI.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Arousal,Arousal: physiology,Brain,Brain Mapping,Brain: blood supply,Evoked Potentials,Evoked Potentials: physiology,Hemodynamics,Hemodynamics: physiology,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Stochastic Processes},
month = {nov},
number = {5},
pages = {607--19},
pmid = {10547338},
title = {{Stochastic designs in event-related fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10547338},
volume = {10},
year = {1999}
}
@article{Durnez2013a,
abstract = {Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive-that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.},
author = {Durnez, Joke and Moerkerke, Beatrijs and Bartsch, Andreas Joachim and Nichols, Thomas E},
doi = {10.3758/s13415-013-0185-3},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Durnez et al. - 2013 - Alternative-based thresholding with application to presurgical fMRI.pdf:pdf},
issn = {1531-135X},
journal = {Cognitive, affective {\&} behavioral neuroscience},
keywords = {Algorithms,Brain,Brain Mapping,Brain: blood supply,Brain: physiology,Brain: surgery,Computer-Assisted,Humans,Image Processing,Linear Models,Magnetic Resonance Imaging,Oxygen,Oxygen: blood,Principal Component Analysis},
month = {dec},
number = {4},
pages = {703--13},
pmid = {23868644},
title = {{Alternative-based thresholding with application to presurgical fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23868644},
volume = {13},
year = {2013}
}
@article{Wager2003,
author = {Wager, Tor D. and Nichols, Thomas E.},
doi = {10.1016/S1053-8119(02)00046-0},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wager, Nichols - 2003 - Optimization of experimental design in fMRI a general framework using a genetic algorithm.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
keywords = {counterbalancing,efficiency,experimental design,fmri,genetic algorithm,neuroimaging methods,optimization},
month = {feb},
number = {2},
pages = {293--309},
title = {{Optimization of experimental design in fMRI: a general framework using a genetic algorithm}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811902000460},
volume = {18},
year = {2003}
}
@article{Friston1996,
abstract = {This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtaining c, or more, clusters with k, or more, voxels, above a threshold u. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion of set-level inference. Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigning P values to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clusters c = 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e., k = 0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster-and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signal f (relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences if f is large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. If f is small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.},
author = {Friston, Karl J and Holmes, A and Poline, J B and Price, C J and Frith, C D},
doi = {10.1006/nimg.1996.0074},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friston et al. - 1996 - Detecting activations in PET and fMRI levels of inference and power.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Brain,Brain Mapping,Brain: physiology,Data Interpretation,Emission-Computed,Emission-Computed: statistics {\&} numeri,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Mathematical Computing,Normal Distribution,Probability,ROC Curve,Sensitivity and Specificity,Statistical,Tomography},
month = {dec},
number = {3 Pt 1},
pages = {223--35},
pmid = {9345513},
title = {{Detecting activations in PET and fMRI: levels of inference and power.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9345513},
volume = {4},
year = {1996}
}
@article{Seurinck2011,
abstract = {A growing number of studies show that visual mental imagery recruits the same brain areas as visual perception. Although the necessity of hV5/MT+ for motion perception has been revealed by means of TMS, its relevance for motion imagery remains unclear. We induced a direction-selective adaptation in hV5/MT+ by means of an MAE while subjects performed a mental rotation task that elicits imagined motion. We concurrently measured behavioral performance and neural activity with fMRI, enabling us to directly assess the effect of a perturbation of hV5/MT+ on other cortical areas involved in the mental rotation task. The activity in hV5/MT+ increased as more mental rotation was required, and the perturbation of hV5/MT+ affected behavioral performance as well as the neural activity in this area. Moreover, several regions in the posterior parietal cortex were also affected by this perturbation. Our results show that hV5/MT+ is required for imagined visual motion and engages in an interaction with parietal cortex during this cognitive process.},
author = {Seurinck, Ruth and de Lange, Floris P and Achten, Erik and Vingerhoets, Guy},
doi = {10.1162/jocn.2010.21525},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Seurinck et al. - 2011 - Mental rotation meets the motion aftereffect the role of hV5MT in visual mental imagery.pdf:pdf},
issn = {1530-8898},
journal = {Journal of cognitive neuroscience},
keywords = {Adult,Humans,Imagination,Imagination: physiology,Male,Mental Processes,Mental Processes: physiology,Motion Perception,Motion Perception: physiology,Photic Stimulation,Photic Stimulation: methods,Psychomotor Performance,Psychomotor Performance: physiology,Rotation,Visual Cortex,Visual Cortex: physiology,Young Adult},
month = {jun},
number = {6},
pages = {1395--404},
pmid = {20521853},
title = {{Mental rotation meets the motion aftereffect: the role of hV5/MT+ in visual mental imagery.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20521853},
volume = {23},
year = {2011}
}
@article{Durnez2013,
abstract = {Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive-that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.},
author = {Durnez, Joke and Moerkerke, Beatrijs and Bartsch, Andreas Joachim and Nichols, Thomas E},
doi = {10.3758/s13415-013-0185-3},
file = {:Users/Joke/Dropbox/doctoraat{\_}Joke/Studie{\_}3{\_}presurgical/inpress.pdf:pdf},
issn = {1531-135X},
journal = {Cognitive, affective {\&} behavioral neuroscience},
keywords = {false negative errors,fmri,multiple,power,pre-surgical fmri,testing},
month = {dec},
number = {4},
pages = {703--13},
pmid = {23868644},
title = {{Alternative-based thresholding with application to presurgical fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23868644},
volume = {13},
year = {2013}
}
@article{Durnez2014,
abstract = {When analyzing functional MRI data, several thresholding procedures are available to account for the huge number of volume units or features that are tested simultaneously. The main focus of these methods is to prevent an inflation of false positives. However, this comes with a serious decrease in power and leads to a problematic imbalance between type I and type II errors. In this paper, we show how estimating the number of activated peaks or clusters enables one to estimate post-hoc how powerful the selection procedure performs. This procedure can be used in real studies as a diagnostics tool, and raises awareness on how much activation is potentially missed. The method is evaluated and illustrated using simulations and a real data example. Our real data example illustrates the lack of power in current fMRI research.},
author = {Durnez, Joke and Moerkerke, Beatrijs and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2013.07.072},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Durnez, Moerkerke, Nichols - 2014 - Post-hoc power estimation for topological inference in fMRI.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {False negative errors,Multiple testing,Power,Random field theory,fMRI},
month = {jan},
pages = {45--64},
pmid = {23927901},
publisher = {Elsevier Inc.},
title = {{Post-hoc power estimation for topological inference in fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23927901},
volume = {84},
year = {2014}
}
@article{Durnez2014a,
abstract = {Functional Magnetic Resonance Imaging is a widespread technique in cognitive psychology that allows visualizing brain activation. The data analysis encompasses an enormous number of simultaneous statistical tests. Procedures that either control the familywise error rate or the false discovery rate have been applied to these data. These methods are mostly validated in terms of average sensitivity and specificity. However, procedures are not comparable if requirements on their error rates differ. Moreover, less attention has been given to the instability or variability of results. In a simulation study in the context of imaging, we first compare the Bonferroni and Benjamini-Hochberg procedures. Considering Bonferroni as a way to control the expected number of type I errors enables more lenient thresholding compared to familywise error rate control and a direct comparison between both procedures. We point out that while the same balance is obtained between average sensitivity and specificity, the Benjamini-Hochberg procedure appears less stable. Secondly, we have implemented the procedure of Gordon et al. () (originally proposed for gene selection) that includes stability, measured through bootstrapping, in the decision criterion. Simulations indicate that the method attains the same balance between sensitivity and specificity. It improves the stability of Benjamini-Hochberg but does not outperform Bonferroni, making this computationally heavy bootstrap procedure less appealing. Third, we show how stability of thresholding procedures can be assessed using real data. In a dataset on face recognition, we again find that Bonferroni renders more stable results.},
author = {Durnez, Joke and Roels, Sanne P and Moerkerke, Beatrijs},
doi = {10.1002/bimj.201200056},
file = {:Users/Joke/Dropbox/doctoraat{\_}Joke/Studie{\_}1{\_}stability/gepubliceerd.pdf:pdf},
issn = {1521-4036},
journal = {Biometrical journal. Biometrische Zeitschrift},
keywords = {additional supporting information may,article,at the publisher,be found in the,fmri,multiple testing,neuroimaging,online version of this,s web-site,sensitivity,stability},
month = {may},
pages = {1--13},
pmid = {24804953},
title = {{Multiple testing in fMRI: An empirical case study on the balance between sensitivity, specificity, and stability.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24804953},
volume = {00},
year = {2014}
}
@article{Mikl2008,
abstract = {The analysis of functional magnetic resonance imaging (fMRI) data involves multiple stages of data pre-processing before the activation can be statistically detected. Spatial smoothing is a very common pre-processing step in the analysis of functional brain imaging data. This study presents a broad perspective on the influence of spatial smoothing on fMRI group activation results. The data obtained from 20 volunteers during a visual oddball task were used for this study. Spatial smoothing using an isotropic gaussian filter kernel with full width at half maximum (FWHM) sizes 2 to 30 mm with a step of 2 mm was applied in two levels - smoothing of fMRI data and/or smoothing of single-subject contrast files prior to general linear model random-effects group analysis generating statistical parametric maps. Five regions of interest were defined, and several parameters (coordinates of nearest local maxima, t value, corrected threshold, effect size, residual values, etc.) were evaluated to examine the effects of spatial smoothing. The optimal filter size for group analysis is discussed according to various criteria. For our experiment, the optimal FWHM is about 8 mm. We can conclude that for robust experiments and an adequate number of subjects in the study, the optimal FWHM for single-subject inference is similar to that for group inference (about 8 mm, according to spatial resolution). For less robust experiments and fewer subjects in the study, a higher FWHM would be optimal for group inference than for single-subject inferences.},
author = {Mikl, Michal and Marecek, Radek and Hlust{\'{i}}k, Petr and Pavlicov{\'{a}}, Martina and Drastich, Ales and Chlebus, Pavel and Br{\'{a}}zdil, Milan and Krupa, Petr},
doi = {10.1016/j.mri.2007.08.006},
issn = {0730-725X},
journal = {Magnetic resonance imaging},
keywords = {Adult,Algorithms,Artifacts,Brain,Brain Mapping,Brain Mapping: instrumentation,Brain Mapping: methods,Brain: pathology,Contrast Media,Equipment Design,Female,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models, Statistical,Normal Distribution,Reproducibility of Results},
month = {may},
number = {4},
pages = {490--503},
pmid = {18060720},
title = {{Effects of spatial smoothing on fMRI group inferences.}},
volume = {26},
year = {2008}
}
@misc{TheMendeleySupportTeam2011b,
abstract = {A quick introduction to Mendeley. Learn how Mendeley creates your personal digital library, how to organize and annotate documents, how to collaborate and share with colleagues, and how to generate citations and bibliographies.},
address = {London},
author = {{The Mendeley Support Team}},
booktitle = {Mendeley Desktop},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/The Mendeley Support Team - 2011 - Getting Started with Mendeley.pdf:pdf},
keywords = {Mendeley,how-to,user manual},
pages = {1--16},
publisher = {Mendeley Ltd.},
title = {{Getting Started with Mendeley}},
url = {http://www.mendeley.com},
year = {2011}
}
@misc{TheMendeleySupportTeam2011c,
abstract = {A quick introduction to Mendeley. Learn how Mendeley creates your personal digital library, how to organize and annotate documents, how to collaborate and share with colleagues, and how to generate citations and bibliographies.},
address = {London},
author = {{The Mendeley Support Team}},
booktitle = {Mendeley Desktop},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/The Mendeley Support Team - 2011 - Getting Started with Mendeley.pdf:pdf},
keywords = {Mendeley,how-to,user manual},
pages = {1--16},
publisher = {Mendeley Ltd.},
title = {{Getting Started with Mendeley}},
url = {http://www.mendeley.com},
year = {2011}
}
@article{Holm1979,
author = {Holm, Sture},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Holm - 1979 - A simple sequentially rejective multiple test procedure.pdf:pdf},
journal = {Scandinavian Journal of Statistics},
keywords = {exact definition,multiple test,simultaneous test,the methodological motivation and},
number = {2},
pages = {65--70},
title = {{A simple sequentially rejective multiple test procedure}},
volume = {6},
year = {1979}
}
@article{Quinlan2013,
author = {Quinlan, Philip T},
doi = {10.1038/nrn3475},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Quinlan - 2013 - Misuse of power in defence of small-scale science.pdf:pdf},
journal = {Nature reviews. Neuroscience},
pages = {2013},
publisher = {Nature Publishing Group},
title = {{Misuse of power : in defence of small-scale science}},
url = {http://dx.doi.org/10.1038/nrn3475-c1},
volume = {237},
year = {2013}
}
@article{Mumford2012,
abstract = {In the past, power analyses were not that common for fMRI studies, but recent advances in power calculation techniques and software development are making power analyses much more accessible. As a result, power analyses are more commonly expected in grant applications proposing fMRI studies. Even though the software is somewhat automated, there are important decisions to be made when setting up and carrying out a power analysis. This guide provides tips on carrying out power analyses, including obtaining pilot data, defining a region of interest and other choices to help create reliable power calculations.},
author = {Mumford, Jeanette A},
doi = {10.1093/scan/nss059},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Mumford - 2012 - A power calculation guide for fMRI studies.pdf:pdf},
issn = {1749-5024},
journal = {Social cognitive and affective neuroscience},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: blood supply,Brain: physiology,Computer-Assisted,Humans,Image Processing,Magnetic Resonance Imaging,Oxygen,Oxygen: blood},
month = {aug},
number = {6},
pages = {738--42},
pmid = {22641837},
title = {{A power calculation guide for fMRI studies.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3427872{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2012}
}
@article{Ashton2013,
author = {Ashton, John C},
doi = {10.1038/nrn3475},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Ashton - 2013 - Experimental power comes from powerful theories — the real problem in null hypothesis testing.pdf:pdf},
journal = {nature},
pages = {2013},
title = {{Experimental power comes from powerful theories — the real problem in null hypothesis testing}},
volume = {834},
year = {2013}
}
@article{Mumford2009,
abstract = {While many advanced mixed-effects models have been proposed and are used in fMRI, the simplest, ordinary least squares (OLS), is still the one that is most widely used. A survey of 90 papers found that 92{\%} of group fMRI analyses used OLS. Despite the widespread use, this simple approach has never been thoroughly justified and evaluated; for example, the typical reference for the method is a conference abstract, (Holmes, A., Friston, K., 1998. Generalisability, random effects {\&} population inference. NeuroImage 7 (4 (2/3)), S754, proceedings of Fourth International Conference on Functional Mapping of the Human Brain, June 7-12, 1998, Montreal, Canada.), which has been referenced over 400 times. In this work we fully derive the simplified method in a general setting and carefully identify the homogeneity assumptions it is based on. We examine the specificity (Type I error rate) of the OLS method under heterogeneity in the one-sample case and find that the OLS method is valid, with only slight conservativeness. Surprisingly, a Satterthwaite approximation for effective degrees of freedom only makes the method more conservative, instead of more accurate. While other authors have highlighted the inferior power of the OLS method relative to optimal mixed-effects methods under heterogeneity, we revisit these results and find the power differences very modest. While statistical methods that make the best use of the data are always to be preferred, software or other practical concerns may require the use of the simple OLS group modeling. In such cases, we find that group mean inferences will be valid under the null hypothesis and will have nearly optimal sensitivity under the alternative.},
author = {Mumford, Jeanette A and Nichols, Thomas},
doi = {10.1016/j.neuroimage.2009.05.034},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Mumford, Nichols - 2009 - Simple group fMRI modeling and inference.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Adult,Brain Mapping,Brain Mapping: methods,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Evoked Potentials,Female,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models,Motor,Motor Cortex,Motor Cortex: physiology,Motor: physiology,Movement,Movement: physiology,Neurological,Reproducibility of Results,Sensitivity and Specificity},
month = {oct},
number = {4},
pages = {1469--75},
pmid = {19463958},
publisher = {Elsevier Inc.},
title = {{Simple group fMRI modeling and inference.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2719771{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {47},
year = {2009}
}
@article{Bacchetti2013,
author = {Bacchetti, Peter},
doi = {10.1038/nrn3475},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bacchetti - 2013 - Small sample size is not the real problem.pdf:pdf},
journal = {Nature reviews. Neuroscience},
pages = {2013},
title = {{Small sample size is not the real problem}},
volume = {24},
year = {2013}
}
@article{Button2013a,
abstract = {A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.},
author = {Button, Katherine S and Ioannidis, John P a and Mokrysz, Claire and Nosek, Brian a and Flint, Jonathan and Robinson, Emma S J and Munaf{\`{o}}, Marcus R},
doi = {10.1038/nrn3475},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Button et al. - 2013 - Power failure why small sample size undermines the reliability of neuroscience(2).pdf:pdf},
issn = {1471-0048},
journal = {Nature reviews. Neuroscience},
keywords = {Humans,Neurosciences,Probability,Reproducibility of Results,Sample Size},
month = {may},
number = {5},
pages = {365--76},
pmid = {23571845},
title = {{Power failure: why small sample size undermines the reliability of neuroscience.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23571845},
volume = {14},
year = {2013}
}
@article{Vul2009a,
author = {Vul, Edward and Harris, Christine and Winkielman, Piotr and Pashler, Harold},
doi = {10.1111/j.1745-6924.2009.01132.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Vul et al. - 2009 - Reply to Comments on “Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition.pdf:pdf},
issn = {17456916},
journal = {Perspectives on Psychological Science},
month = {may},
number = {3},
pages = {319--324},
title = {{Reply to Comments on “Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition”}},
url = {http://pps.sagepub.com/lookup/doi/10.1111/j.1745-6924.2009.01132.x},
volume = {4},
year = {2009}
}
@article{Wacholder2004,
author = {Wacholder, S. and Chanock, S. and Garcia-Closas, M. and El ghormli, L. and Rothman, N.},
doi = {10.1093/jnci/djh075},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wacholder et al. - 2004 - Assessing the Probability That a Positive Report is False An Approach for Molecular Epidemiology Studies.pdf:pdf},
issn = {0027-8874},
journal = {JNCI Journal of the National Cancer Institute},
month = {mar},
number = {6},
pages = {434--442},
title = {{Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies}},
url = {http://jnci.oxfordjournals.org/cgi/doi/10.1093/jnci/djh075},
volume = {96},
year = {2004}
}
@article{Joyce2012,
abstract = {Although there are a number of statistical software tools for voxel-based massively univariate analysis of neuroimaging data, such as fMRI (functional MRI), PET (positron emission tomography), and VBM (voxel-based morphometry), very few software tools exist for power and sample size calculation for neuroimaging studies. Unlike typical biomedical studies, outcomes from neuroimaging studies are 3D images of correlated voxels, requiring a correction for massive multiple comparisons. Thus, a specialized power calculation tool is needed for planning neuroimaging studies. To facilitate this process, we developed a software tool specifically designed for neuroimaging data. The software tool, called PowerMap, implements theoretical power calculation algorithms based on non-central random field theory. It can also calculate power for statistical analyses with FDR (false discovery rate) corrections. This GUI (graphical user interface)-based tool enables neuroimaging researchers without advanced knowledge in imaging statistics to calculate power and sample size in the form of 3D images. In this paper, we provide an overview of the statistical framework behind the PowerMap tool. Three worked examples are also provided, a regression analysis, an ANOVA (analysis of variance), and a two-sample T-test, in order to demonstrate the study planning process with PowerMap. We envision that PowerMap will be a great aide for future neuroimaging research.},
author = {Joyce, Karen E and Hayasaka, Satoru},
doi = {10.1007/s12021-012-9152-3},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Joyce, Hayasaka - 2012 - Development of PowerMap a software package for statistical power calculation in neuroimaging studies.pdf:pdf},
isbn = {1202101291},
issn = {1559-0089},
journal = {Neuroinformatics},
keywords = {Adolescent,Adult,Aged,Algorithms,Analysis of Variance,Brain,Brain Mapping,Brain: anatomy {\&} histology,Brain: blood supply,Female,Humans,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Male,Middle Aged,Neuroimaging,Neuroimaging: methods,Regression Analysis,Software,Young Adult},
month = {oct},
number = {4},
pages = {351--65},
pmid = {22644868},
title = {{Development of PowerMap: a software package for statistical power calculation in neuroimaging studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22644868},
volume = {10},
year = {2012}
}
@article{Chang2011,
abstract = {Sensory gating deficit in schizophrenia patients has been well-documented. However, a central conceptual issue, regarding whether the gating deficit results from an abnormal initial response (S1) or difficulty in attenuating the response to the repeating stimulus (S2), raise doubts about the validity and utility of the S2/S1 ratio as a measure of sensory gating. This meta-analysis study, therefore, sought to determine the consistency and relative magnitude of the effect of the two essential components (S1 and S2) and the ratio. The results of weighted random effects meta-analysis revealed that the overall effect sizes for the S1 amplitude, S2 amplitude, and P50 S2/S1 ratio were -0.19 (small), 0.65 (medium to large), and 0.93 (large), respectively. These results confirm that the S2/S1 ratio and the repeating (S2) stimulus differ robustly between schizophrenia patients and healthy controls in contrast to the consistent but smaller effect size for the S1 amplitude. These findings are more likely to reflect defective inhibition of repeating redundant input rather than an abnormal response to novel stimuli.},
author = {Chang, Wen-Pin and Arfken, Cynthia L and Sangal, Monica P and Boutros, Nash N},
doi = {10.1111/j.1469-8986.2010.01168.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Chang et al. - 2011 - Probing the relative contribution of the first and second responses to sensory gating indices a meta-analysis.pdf:pdf},
issn = {1540-5958},
journal = {Psychophysiology},
keywords = {Acoustic Stimulation,Brain,Brain: physiopathology,Electroencephalography,Evoked Potentials, Auditory,Evoked Potentials, Auditory: physiology,Humans,Reaction Time,Reaction Time: physiology,Schizophrenia,Schizophrenia: physiopathology,Sensory Gating,Sensory Gating: physiology},
month = {jul},
number = {7},
pages = {980--92},
pmid = {21214588},
title = {{Probing the relative contribution of the first and second responses to sensory gating indices: a meta-analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21214588},
volume = {48},
year = {2011}
}
@article{Mumford2008a,
abstract = {When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80{\%} power is not as simple as finding the smallest sample size capable of achieving 80{\%} power, since smaller sample sizes require each subject to be scanned longer.},
author = {Mumford, Jeanette A and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2007.07.061},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Mumford, Nichols - 2008 - Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation(2).pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Algorithms,Auditory,Auditory: physiology,Brain Mapping,Brain Mapping: methods,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Evoked Potentials,Female,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models,Neurological,Reproducibility of Results,Sensitivity and Specificity,Speech Perception,Speech Perception: physiology,Statistics as Topic},
month = {jan},
number = {1},
pages = {261--8},
pmid = {17919925},
title = {{Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2423281{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {39},
year = {2008}
}
@article{Maus2011,
abstract = {The design of a multi-subject fMRI experiment needs specification of the number of subjects and scanning time per subject. For example, for a blocked design with conditions A or B, fixed block length and block order ABN, where N denotes a null block, the optimal number of cycles of ABN and the optimal number of subjects have to be determined. This paper presents a method to determine the optimal number of subjects and optimal number of cycles for a blocked design based on the A-optimality criterion and a linear cost function by which the number of cycles and the number of subjects are restricted. Estimation of individual stimulus effects and estimation of contrasts between stimulus effects are both considered. The mixed-effects model is applied and analytical results for the A-optimal number of subjects and A-optimal number of cycles are obtained under the assumption of uncorrelated errors. For correlated errors with a first-order autoregressive (AR1) error structure, numerical results are presented. Our results show how the optimal number of cycles and subjects depend on the within- to between-subject variance ratio. Our method is a new approach to determine the optimal scanning time and optimal number of subjects for a multi-subject fMRI experiment. In contrast to previous results based on power analyses, the optimal number of cycles and subjects can be described analytically and costs are considered.},
author = {Maus, B{\"{a}}rbel and van Breukelen, Gerard J P and Goebel, Rainer and Berger, Martijn P F},
doi = {10.1016/j.neuroimage.2011.03.019},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Maus et al. - 2011 - Optimal design of multi-subject blocked fMRI experiments.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Budgets,Data Interpretation, Statistical,Hemodynamics,Hemodynamics: physiology,Humans,Least-Squares Analysis,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: economics,Magnetic Resonance Imaging: methods,Magnetic Resonance Imaging: statistics {\&} numerical,Research,Research Design,Research: economics,Sample Size},
month = {jun},
number = {3},
pages = {1338--52},
pmid = {21406234},
publisher = {Elsevier Inc.},
title = {{Optimal design of multi-subject blocked fMRI experiments.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21406234},
volume = {56},
year = {2011}
}
@article{Carp2012,
abstract = {Replication of research findings is critical to the progress of scientific understanding. Accordingly, most scientific journals require authors to report experimental procedures in sufficient detail for independent researchers to replicate their work. To what extent do research reports in the functional neuroimaging literature live up to this standard? The present study evaluated methods reporting and methodological choices across 241 recent fMRI articles. Many studies did not report critical methodological details with regard to experimental design, data acquisition, and analysis. Further, many studies were underpowered to detect any but the largest statistical effects. Finally, data collection and analysis methods were highly flexible across studies, with nearly as many unique analysis pipelines as there were studies in the sample. Because the rate of false positive results is thought to increase with the flexibility of experimental designs, the field of functional neuroimaging may be particularly vulnerable to false positives. In sum, the present study documented significant gaps in methods reporting among fMRI studies. Improved methodological descriptions in research reports would yield significant benefits for the field.},
author = {Carp, Joshua},
doi = {10.1016/j.neuroimage.2012.07.004},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Carp - 2012 - The secret lives of experiments methods reporting in the fMRI literature.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Biomedical Research,Biomedical Research: statistics {\&} numerical data,Brain Mapping,Brain Mapping: statistics {\&} numerical data,Humans,Literature Based Discovery,Literature Based Discovery: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Periodicals as Topic,Periodicals as Topic: statistics {\&} numerical data,Reproducibility of Results,Sensitivity and Specificity},
month = {oct},
number = {1},
pages = {289--300},
pmid = {22796459},
publisher = {Elsevier Inc.},
title = {{The secret lives of experiments: methods reporting in the fMRI literature.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22796459},
volume = {63},
year = {2012}
}
@article{Ingre2013,
abstract = {It is sometimes argued that small studies provide better evidence for reported effects because they are less likely to report findings with small and trivial effect sizes (Friston, 2012). But larger studies are actually better at protecting against inferences from trivial effect sizes, if researchers just make use of effect sizes and confidence intervals. Poor statistical power also comes at a cost of inflated proportion of false positive findings, less power to "confirm" true effects and bias in reported (inflated) effect sizes. Small studies (n=16) lack the precision to reliably distinguish small and medium to large effect sizes (r{\textless}.50) from random noise ($\alpha$=.05) that larger studies (n=100) does with high level of confidence (r=.50, p=.00000012). The present paper presents the arguments needed for researchers to refute the claim that small low-powered studies have a higher degree of scientific evidence than large high-powered studies.},
author = {Ingre, Michael},
doi = {10.1016/j.neuroimage.2013.03.030},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Ingre - 2013 - Why small low-powered studies are worse than large high-powered studies and how to protect against trivial findings(2012).pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {False positive findings,Inflated effect sizes,Statistical power},
month = {nov},
pages = {496--8},
pmid = {23583358},
publisher = {Elsevier Inc.},
title = {{Why small low-powered studies are worse than large high-powered studies and how to protect against "trivial" findings in research: comment on Friston (2012).}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23583358},
volume = {81},
year = {2013}
}
@article{,
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Unknown - Unknown - Afdeling Studentenadministratie en studieprogramma ' s Registratie werkstudent.pdf:pdf},
pages = {9000},
title = {{Afdeling Studentenadministratie en studieprogramma ' s Registratie werkstudent}},
volume = {33}
}
@article{Button2013,
abstract = {A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.},
author = {Button, Katherine S and Ioannidis, John P a and Mokrysz, Claire and Nosek, Brian a and Flint, Jonathan and Robinson, Emma S J and Munaf{\`{o}}, Marcus R},
doi = {10.1038/nrn3475},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Button et al. - 2013 - Power failure why small sample size undermines the reliability of neuroscience.pdf:pdf},
issn = {1471-0048},
journal = {Nature reviews. Neuroscience},
month = {may},
number = {5},
pages = {365--76},
pmid = {23571845},
title = {{Power failure: why small sample size undermines the reliability of neuroscience.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23571845},
volume = {14},
year = {2013}
}
@article{Friman2003,
author = {Friman, Ola and Borga, Magnus and Lundberg, Peter and Knutsson, Hans},
doi = {10.1016/S1053-8119(03)00077-6},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friman et al. - 2003 - Adaptive analysis of fMRI data.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
month = {jul},
number = {3},
pages = {837--845},
title = {{Adaptive analysis of fMRI data}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811903000776},
volume = {19},
year = {2003}
}
@article{Dwass1957,
author = {Dwass, M.},
journal = {Annals of Mathematical Statistics},
pages = {181--187},
title = {{Modified randomization tests for nonparametric hypotheses.}},
volume = {28},
year = {1957}
}
@article{Bullmore1996,
abstract = {Two questions arising in the analysis of functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation are: i) how to measure the experimentally determined effect in fMRI time series; and ii) how to decide whether an apparent effect is significant. Our approach is first to fit a time series regression model, including sine and cosine terms at the (fundamental) frequency of experimental stimulation, by pseudogeneralized least squares (PGLS) at each pixel of an image. Sinusoidal modeling takes account of locally variable hemodynamic delay and dispersion, and PGLS fitting corrects for residual or endogenous autocorrelation in fMRI time series, to yield best unbiased estimates of the amplitudes of the sine and cosine terms at fundamental frequency; from these parameters the authors derive estimates of experimentally determined power and its standard error. Randomization testing is then used to create inferential brain activation maps (BAMs) of pixels significantly activated by the experimental stimulus. The methods are illustrated by application to data acquired from normal human subjects during periodic visual and auditory stimulation.},
author = {Bullmore, E and Brammer, M and Williams, S C and Rabe-Hesketh, S and Janot, N and David, a and Mellers, J and Howard, R and Sham, P},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bullmore et al. - 1996 - Statistical methods of estimation and inference for functional MR image analysis.pdf:pdf},
issn = {0740-3194},
journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
keywords = {Acoustic Stimulation,Brain,Brain Mapping,Brain: physiology,Echo-Planar Imaging,Echo-Planar Imaging: methods,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Photic Stimulation,Regression Analysis,Sensitivity and Specificity},
month = {feb},
number = {2},
pages = {261--77},
pmid = {8622592},
title = {{Statistical methods of estimation and inference for functional MR image analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/8622592},
volume = {35},
year = {1996}
}
@misc{,
title = {{Bullmore et al. 1996pdf.PDF}}
}
@misc{TheMendeleySupportTeam2011a,
abstract = {A quick introduction to Mendeley. Learn how Mendeley creates your personal digital library, how to organize and annotate documents, how to collaborate and share with colleagues, and how to generate citations and bibliographies.},
address = {London},
author = {{The Mendeley Support Team}},
booktitle = {Mendeley Desktop},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/The Mendeley Support Team - 2011 - Getting Started with Mendeley.pdf:pdf},
keywords = {Mendeley,how-to,user manual},
pages = {1--16},
publisher = {Mendeley Ltd.},
title = {{Getting Started with Mendeley}},
url = {http://www.mendeley.com},
year = {2011}
}
@phdthesis{Holmes1995,
author = {Holmes, Andrew P},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Holmes - 1995 - Statistical Issues in functional brain mapping.pdf:pdf},
school = {Unversity of Glasgow},
title = {{Statistical Issues in functional brain mapping}},
year = {1995}
}
@article{Biswal2010,
abstract = {Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency ({\textless}0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon{\_}1000/.},
author = {Biswal, Bharat B and Mennes, Maarten and Zuo, Xi-Nian and Gohel, Suril and Kelly, Clare and Smith, Stephen M and Beckmann, Christian F and Adelstein, Jonathan S and Buckner, Randy L and Colcombe, Stan and Dogonowski, Anne-Marie and Ernst, Monique and Fair, Damien and Hampson, Michelle and Hoptman, Matthew J and Hyde, James S and Kiviniemi, Vesa J and K{\"{o}}tter, Rolf and Li, Shi-Jiang and Lin, Ching-Po and Lowe, Mark J and Mackay, Clare and Madden, David J and Madsen, Kristoffer H and Margulies, Daniel S and Mayberg, Helen S and McMahon, Katie and Monk, Christopher S and Mostofsky, Stewart H and Nagel, Bonnie J and Pekar, James J and Peltier, Scott J and Petersen, Steven E and Riedl, Valentin and Rombouts, Serge a R B and Rypma, Bart and Schlaggar, Bradley L and Schmidt, Sein and Seidler, Rachael D and Siegle, Greg J and Sorg, Christian and Teng, Gao-Jun and Veijola, Juha and Villringer, Arno and Walter, Martin and Wang, Lihong and Weng, Xu-Chu and Whitfield-Gabrieli, Susan and Williamson, Peter and Windischberger, Christian and Zang, Yu-Feng and Zhang, Hong-Ying and Castellanos, F Xavier and Milham, Michael P},
doi = {10.1073/pnas.0911855107},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Biswal et al. - 2010 - Toward discovery science of human brain function.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Adolescent,Adult,Age Factors,Aged,Algorithms,Analysis of Variance,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Middle Aged,Neural Pathways,Neural Pathways: anatomy {\&} histology,Neural Pathways: physiology,Sex Factors,Young Adult},
month = {mar},
number = {10},
pages = {4734--9},
pmid = {20176931},
title = {{Toward discovery science of human brain function.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2842060{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {107},
year = {2010}
}
@article{Adler2010,
author = {Adler, Robert J. and Bobrowski, Omer and Borman, Matthew S},
doi = {10.1214/10-IMSCOLL609},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Adler, Bobrowski, Borman - 2010 - Persistent homology for random fields and complexes.pdf:pdf},
journal = {IMS Collections. Borrowing SStrength: Theory Powering Applications - A Festschrift for Lawrence D. Brown},
pages = {124--143},
title = {{Persistent homology for random fields and complexes}},
volume = {6},
year = {2010}
}
@article{Eklund2011,
abstract = {Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.},
author = {Eklund, Anders and Andersson, Mats and Knutsson, Hans},
doi = {10.1155/2011/627947},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Eklund, Andersson, Knutsson - 2011 - Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analys.pdf:pdf},
issn = {1687-4196},
journal = {International journal of biomedical imaging},
month = {jan},
pages = {627947},
pmid = {22046176},
title = {{Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3199190{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {2011},
year = {2011}
}
@article{Raz2003,
author = {Raz, Jonathan and Zheng, Hui and Ombao, Hernando and Turetsky, Bruce},
doi = {10.1016/S1053-8119(03)00115-0},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Raz et al. - 2003 - Statistical tests for fMRI based on experimental randomization.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
month = {jun},
number = {2},
pages = {226--232},
title = {{Statistical tests for fMRI based on experimental randomization}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811903001150},
volume = {19},
year = {2003}
}
@article{Eklund2012,
abstract = {The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5{\%}, significant activity was found in 1{\%}-70{\%} of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1{\%}-19{\%} of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.},
author = {Eklund, Anders and Andersson, Mats and Josephson, Camilla and Johannesson, Magnus and Knutsson, Hans},
doi = {10.1016/j.neuroimage.2012.03.093},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Eklund et al. - 2012 - Does parametric fMRI analysis with SPM yield valid results An empirical study of 1484 rest datasets.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Adolescent,Adult,Aged,Aged, 80 and over,Brain Mapping,Brain Mapping: methods,Databases, Factual,Evoked Potentials,Evoked Potentials: physiology,Female,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Logistic Models,Magnetic Resonance Imaging,Male,Middle Aged,Movement,Movement: physiology,Normal Distribution,Reproducibility of Results,Rest,Rest: physiology,Software,Young Adult},
month = {jul},
number = {3},
pages = {565--78},
pmid = {22507229},
publisher = {Elsevier Inc.},
title = {{Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22507229},
volume = {61},
year = {2012}
}
@article{Ioannidis2005,
abstract = {There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.},
author = {Ioannidis, John P a},
doi = {10.1371/journal.pmed.0020124},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Ioannidis - 2005 - Why most published research findings are false.pdf:pdf},
issn = {1549-1676},
journal = {PLoS medicine},
keywords = {Bias (Epidemiology),Data Interpretation, Statistical,Likelihood Functions,Meta-Analysis as Topic,Odds Ratio,Publishing,Reproducibility of Results,Research Design,Sample Size},
month = {aug},
number = {8},
pages = {e124},
pmid = {16060722},
title = {{Why most published research findings are false.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1182327{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {2},
year = {2005}
}
@article{Owen2005,
author = {Owen, Art B.},
doi = {10.1111/j.1467-9868.2005.00509.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Owen - 2005 - Variance of the number of false discoveries.pdf:pdf},
issn = {1369-7412},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
keywords = {false discovery rate,microarrays,multiple comparisons,polymorphisms,single-nucleotide,step-down},
month = {jun},
number = {3},
pages = {411--426},
title = {{Variance of the number of false discoveries}},
url = {http://doi.wiley.com/10.1111/j.1467-9868.2005.00509.x},
volume = {67},
year = {2005}
}
@article{Storey2013,
author = {Storey, John D and Taylor, Jonathan E and Siegmund, David},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Storey, Taylor, Siegmund - 2004 - Strong control , conservative point estimation and simultaneous conservative consistency of false disc.pdf:pdf},
journal = {Journal of the Royal Statistical Society. Series B, Statistical methodology},
number = {1},
title = {{Strong control , conservative point estimation and simultaneous conservative consistency of false discovery rates : a unified approach}},
volume = {66},
year = {2004}
}
@article{Woolrich2005,
abstract = {Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.},
author = {Woolrich, Mark W and Behrens, Timothy E J and Beckmann, Christian F and Smith, Stephen M},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Woolrich et al. - 2005 - Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.pdf:pdf},
issn = {0278-0062},
journal = {IEEE transactions on medical imaging},
keywords = {Algorithms,Artificial Intelligence,Brain,Brain: anatomy {\&} histology,Cluster Analysis,Computer Simulation,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Information Storage and Retrieval,Information Storage and Retrieval: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Biological,Models, Statistical,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity,Signal Processing, Computer-Assisted},
month = {jan},
number = {1},
pages = {1--11},
pmid = {15638182},
title = {{Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15638182},
volume = {24},
year = {2005}
}
@article{Hommel2008,
abstract = {In this paper we discuss aesthetical concepts and requirements for reasonable multiple test procedures. Aesthetical considerations lead to logical decision patterns which are conceivable and, if possible, simple to use and to communicate. Such considerations are sometimes contradictory to the ubiquitous requirement of maximizing power for a multiple test procedure. We illustrate the necessary trade-offs with several examples. We start by considering important logical properties and then discuss three different concepts of monotonicity. Afterwards we have a closer look at the recently proposed "fallback procedure" and show that it has some less appealing properties. Finally, we investigate the distribution of the numbers of significant results with respect to both expectation and variance.},
author = {Hommel, Gerhard and Bretz, Frank},
doi = {10.1002/bimj.200710463},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hommel, Bretz - 2008 - Aesthetics and power considerations in multiple testing--a contradiction.pdf:pdf},
issn = {1521-4036},
journal = {Biometrical journal. Biometrische Zeitschrift},
keywords = {Biometry,Biometry: methods},
month = {oct},
number = {5},
pages = {657--66},
pmid = {18932151},
title = {{Aesthetics and power considerations in multiple testing--a contradiction?}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18932151},
volume = {50},
year = {2008}
}
@incollection{Glaser2007,
address = {Amsterdam},
author = {Glaser, D. and Friston, Karl J},
booktitle = {Statistical Parametric Mapping},
chapter = {10},
editor = {Friston, Karl J and Ashburner, J and Kiebel, S. J. and Nichols, Thomas E and Penny, W. D.},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Glaser, Friston - 2007 - Covariance components.pdf:pdf},
pages = {140--147},
publisher = {Elsevier},
title = {{Covariance components}},
year = {2007}
}
@article{Gonzalez-Castillo2012,
abstract = {The brain is the body's largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95{\%} of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models.},
author = {Gonzalez-Castillo, Javier and Saad, Ziad S and Handwerker, Daniel a and Inati, Souheil J and Brenowitz, Noah and Bandettini, Peter A.},
doi = {10.1073/pnas.1121049109},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Gonzalez-Castillo et al. - 2012 - Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free.pdf:pdf},
isbn = {1121049109},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Brain,Brain: physiology,Humans,Magnetic Resonance Imaging,Models,Task Performance and Analysis,Theoretical},
month = {apr},
number = {14},
pages = {5487--92},
pmid = {22431587},
title = {{Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3325687{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {109},
year = {2012}
}
@article{Taylor2006,
author = {Taylor, J E},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Taylor - 2006 - Detecting sparse signals in random fields , with an application to brain mapping.pdf:pdf},
pages = {1--38},
title = {{Detecting sparse signals in random fields , with an application to brain mapping}},
volume = {94305},
year = {2006}
}
@article{Yoshiura2001,
abstract = {Despite its immediate success as a tool for basic research, the clinical application of functional MRI(fMRI) is still limited. FMRI has proven useful for presurgical functional mapping of the eloquent cortices. Localization of the sensorimotor cortex by fMRI may be of relatively limited value because the sensorimotor cortex can often be readily localized by means of anatomical methods. In contrast, the language cortices may not be localized anatomically and the language dominant hemisphere has been determined by invasive Wada test. Previous reports have shown that fMRI can be a promising alternative to the Wada test. A recent clinical trial has suggested that fMRI can be used to diagnose Alzheimer's disease in its earliest stage, detecting subclinical deterioration of the memory function. FMRI may be useful to predict the future decline of memory in people with genetic risks. Monitoring of the functional recovery of post-stroke brains may be another promising clinical application of fMRI. FMRI has demonstrated functional reorganization of the brain that may be related to the restoration of motor and language functions.},
author = {Yoshiura, T},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Yoshiura - 2001 - Clinical applications of functional magnetic resonance imaging.pdf:pdf},
issn = {0048-0428},
journal = {Nihon Igaku Hōshasen Gakkai zasshi. Nippon acta radiologica},
keywords = {Brain Diseases,Brain Diseases: diagnosis,Brain Diseases: physiopathology,Humans,Magnetic Resonance Imaging},
month = {jun},
number = {7},
pages = {332--6},
pmid = {11496412},
title = {{[Clinical applications of functional magnetic resonance imaging].}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21150057},
volume = {61},
year = {2001}
}
@article{Worsley1996a,
author = {Worsley, Keith J},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Worsley - 1996 - Vol. 9, No. 1 (Winter 1996) 1.pdf:pdf},
number = {1},
title = {{Vol. 9, No. 1 (Winter 1996) 1}},
volume = {9},
year = {1996}
}
@article{Haxby2012,
abstract = {In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application.},
author = {Haxby, James V},
doi = {10.1016/j.neuroimage.2012.03.016},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Haxby - 2012 - Multivariate pattern analysis of fMRI the early beginnings.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Artificial Intelligence,Brain,Brain Mapping,Brain Mapping: history,Brain Mapping: methods,Brain: physiology,History, 20th Century,History, 21st Century,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: history,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: history,Magnetic Resonance Imaging: methods},
month = {aug},
number = {2},
pages = {852--5},
pmid = {22425670},
publisher = {Elsevier Inc.},
title = {{Multivariate pattern analysis of fMRI: the early beginnings.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22425670},
volume = {62},
year = {2012}
}
@article{Bartsch2006,
abstract = {Functional magnetic resonance imaging (fMRI) has become a popular research tool, yet its use for diagnostic purposes and actual treatment planning has remained less widespread. The literature yields rather sparse evidence-based data on clinical fMRI applications and accordant decision-making. Notwithstanding, blood oxygenation level dependent (BOLD)- and arterial spin labeling (ASL)-fMRI can be judiciously combined with perfusion measurements, electroencephalographic (EEG) recordings, diffusion-weighted imaging (DWI), and fiber tractographies to assist clinical decisions. In this article we provide an overview of clinical fMRI applications based on illustrative examples. Assessment of cochlear implant candidates by fMRI is covered in some detail, and distinct reference is made to particular challenges imposed by brain tumors, other space-occupying lesions, cortical dysplasias, seizure disorders, and vascular malformations. Specific strategies, merits, and pitfalls of analyzing and interpreting diagnostic fMRI studies in individual patients are highlighted.},
author = {Bartsch, Andreas Joachim and Homola, Gy{\"{o}}rgy and Biller, Armin and Solymosi, L{\'{a}}szl{\'{o}} and Bendszus, Martin},
doi = {10.1002/jmri.20579},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bartsch et al. - 2006 - Diagnostic functional MRI illustrated clinical applications and decision-making.pdf:pdf},
issn = {1053-1807},
journal = {Journal of magnetic resonance imaging : JMRI},
keywords = {Brain Diseases,Brain Diseases: diagnosis,Brain Mapping,Brain Mapping: methods,Decision Support Systems, Clinical,Humans,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Nervous System Diseases,Nervous System Diseases: diagnosis},
month = {jun},
number = {6},
pages = {921--32},
pmid = {16649199},
title = {{Diagnostic functional MRI: illustrated clinical applications and decision-making.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16649199},
volume = {23},
year = {2006}
}
@article{Haller2009,
abstract = {Several different techniques allow a functional assessment of neuronal activations by magnetic resonance imaging (fMRI). The by far most influential fMRI technique is based on a local T2*-sensitive hemodynamic response to neuronal activation, also known as the blood oxygenation level dependent or BOLD effect. Consequently, the term 'fMRI' is often used synonymously with BOLD imaging. Because interpretations of fMRI brain activation maps often appear intuitive and compelling, the reader might be tempted not to critically question the fundamental processes and assumptions. We review some essential processes and assumptions of BOLD fMRI and discuss related confounds and pitfalls in fMRI - from the underlying physiological effect, to data acquisition, data analysis and the interpretation of the results including clinical fMRI. A background framework is provided for the systematic and critical interpretation of fMRI results.},
author = {Haller, Sven and Bartsch, Andreas Joachim},
doi = {10.1007/s00330-009-1456-9},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Haller, Bartsch - 2009 - Pitfalls in FMRI.pdf:pdf},
issn = {1432-1084},
journal = {European radiology},
keywords = {Artifacts,Brain,Brain Mapping,Brain Mapping: methods,Brain: pathology,Computer-Assisted,Computer-Assisted: methods,Echo-Planar Imaging,Echo-Planar Imaging: methods,Electroencephalography,Electroencephalography: methods,False Positive Reactions,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Movement,Neurons,Neurons: pathology,Oxygen,Oxygen: chemistry,Principal Component Analysis,Reproducibility of Results,Time Factors},
month = {nov},
number = {11},
pages = {2689--706},
pmid = {19504107},
title = {{Pitfalls in FMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19504107},
volume = {19},
year = {2009}
}
@article{Richardson2004a,
abstract = {Functional MRI (fMRI) of cognitive tasks depends on technology widely available in the clinical sphere, but has yet to show a role in the investigation of patients. We report here the first demonstration of a clinically valuable role for cognitive fMRI. Temporal lobe epilepsy (TLE) is commonly caused by hippocampal sclerosis and is frequently resistant to drug treatment. Surgical resection of the left hippocampus in this setting can cure seizures, but may produce significant verbal memory decline, which is hard to predict. We report 10 right-handed TLE patients with left hippocampal sclerosis who underwent left hippocampal resection. We compared currently used data for the prediction of post-operative verbal memory decline in such patients with a novel fMRI assessment of verbal memory encoding. Multiple regression analyses showed that fMRI provided the strongest independent predictor of memory outcome after surgery. At the individual subject level, the fMRI data had high positive predictive value for memory decline.},
author = {Richardson, Mark P and Strange, Bryan a and Thompson, Pamela J and Baxendale, Sallie a and Duncan, John S and Dolan, Raymond J},
doi = {10.1093/brain/awh293},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Richardson et al. - 2004 - Pre-operative verbal memory fMRI predicts post-operative memory decline after left temporal lobe resection.pdf:pdf},
issn = {1460-2156},
journal = {Brain : a journal of neurology},
keywords = {Adult,Anterior Temporal Lobectomy,Anterior Temporal Lobectomy: adverse effects,Epilepsy, Temporal Lobe,Epilepsy, Temporal Lobe: etiology,Epilepsy, Temporal Lobe: psychology,Epilepsy, Temporal Lobe: surgery,Female,Hippocampus,Hippocampus: pathology,Hippocampus: surgery,Humans,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Memory,Memory Disorders,Memory Disorders: etiology,Middle Aged,Neuropsychological Tests,Preoperative Care,Prognosis,Prospective Studies,Sclerosis},
month = {nov},
number = {Pt 11},
pages = {2419--26},
pmid = {15459025},
title = {{Pre-operative verbal memory fMRI predicts post-operative memory decline after left temporal lobe resection.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15459025},
volume = {127},
year = {2004}
}
@article{Beckmann2012,
abstract = {Independent Component Analysis (ICA) is a computational technique for identifying hidden statistically independent sources from multivariate data. In its basic form, ICA decomposes a 2D data matrix (e.g. time × voxels) into separate components that have distinct characteristics. In FMRI it is used to identify hidden FMRI signals (such as activations). Since the first application of ICA to Functional Magnetic Resonance Imaging (FMRI) in 1998, this technique has developed into a powerful tool for data exploration in cognitive and clinical neurosciences. In this contribution to the commemorative issue 20 years of FMRI I will briefly describe the basic principles behind ICA, discuss the probabilistic extension to ICA and touch on what I think are some of the most notorious loose ends. Further, I will describe some of the most powerful 'killer' applications and finally share some thoughts on where I believe the most promising future developments will lie.},
author = {Beckmann, Christian F},
doi = {10.1016/j.neuroimage.2012.02.020},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Beckmann - 2012 - Modelling with independent components.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Animals,Brain,Brain Mapping,Brain Mapping: history,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Factor Analysis, Statistical,History, 20th Century,History, 21st Century,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: history,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: history,Magnetic Resonance Imaging: methods,Models, Neurological,Models, Theoretical,Principal Component Analysis,Principal Component Analysis: history,Principal Component Analysis: methods},
month = {aug},
number = {2},
pages = {891--901},
pmid = {22369997},
publisher = {Elsevier Inc.},
title = {{Modelling with independent components.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22369997},
volume = {62},
year = {2012}
}
@article{Poline2012,
abstract = {In this review, we first set out the general linear model (GLM) for the non technical reader, as a tool able to do both linear regression and ANOVA within the same flexible framework. We present a short history of its development in the fMRI community, and describe some interesting examples of its early use. We offer a few warnings, as the GLM relies on assumptions that may not hold in all situations. We conclude with a few wishes for the future of fMRI analyses, with or without the GLM. The appendix develops some aspects of use of contrasts for testing for the more technical reader.},
author = {Poline, Jean-Baptiste and Brett, Matthew},
doi = {10.1016/j.neuroimage.2012.01.133},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Poline, Brett - 2012 - The general linear model and fMRI does love last forever.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain Mapping,Brain Mapping: history,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,History, 20th Century,History, 21st Century,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: history,Image Processing, Computer-Assisted: methods,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: history,Magnetic Resonance Imaging: methods},
month = {aug},
number = {2},
pages = {871--80},
pmid = {22343127},
publisher = {Elsevier Inc.},
title = {{The general linear model and fMRI: does love last forever?}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22343127},
volume = {62},
year = {2012}
}
@article{Evans2012,
abstract = {The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.},
author = {Evans, Alan C and Janke, Andrew L and Collins, D Louis and Baillet, Sylvain},
doi = {10.1016/j.neuroimage.2012.01.024},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Evans et al. - 2012 - Brain templates and atlases.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Anatomy, Artistic,Anatomy, Artistic: history,Atlases as Topic,Atlases as Topic: history,Brain,Brain Mapping,Brain Mapping: history,Brain Mapping: methods,Brain: anatomy {\&} histology,History, 20th Century,History, 21st Century,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: history,Image Processing, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: history,Imaging, Three-Dimensional: methods,Neuroimaging,Neuroimaging: history,Neuroimaging: methods},
month = {aug},
number = {2},
pages = {911--22},
pmid = {22248580},
publisher = {Elsevier Inc.},
title = {{Brain templates and atlases.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22248580},
volume = {62},
year = {2012}
}
@article{Woolrich2012,
abstract = {Bayesian inference has taken FMRI methods research into areas that frequentist statistics have struggled to reach. In this article we will consider some of the early forays into Bayes and what motivated its use. We shall see the impact that Bayes has had on haemodynamic modelling, spatial modelling, group analysis, model selection and brain connectivity analysis; and consider how these advancements have spun-off into related areas of neuroscience and some of the challenges that remain. Bayes has brought to the table inference flexibility, incorporation of prior information, adaptive regularisation and model selection. But perhaps more important than these things, is the ability of Bayes to empower the methods researcher with a mathematically principled framework for inferring on any model.},
author = {Woolrich, Mark W},
doi = {10.1016/j.neuroimage.2011.10.047},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Woolrich - 2012 - Bayesian inference in FMRI.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Bayes Theorem,Brain,Brain: anatomy {\&} histology,Brain: physiology,History, 20th Century,History, 21st Century,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: history,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: history,Magnetic Resonance Imaging: methods,Models, Theoretical},
month = {aug},
number = {2},
pages = {801--10},
pmid = {22063092},
publisher = {Elsevier Inc.},
title = {{Bayesian inference in FMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22063092},
volume = {62},
year = {2012}
}
@article{Silver2011,
abstract = {Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In "imaging genetics", such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of 'null' SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5{\%}, false positive rates were found to be well-controlled (3.9-5.6{\%}), using a relatively high cluster-forming threshold, $\alpha$(c)=0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds ($\alpha$(c)=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6{\%}. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases.},
author = {Silver, Matt and Montana, Giovanni and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2010.08.049},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Silver, Montana, Nichols - 2011 - False positives in neuroimaging genetics using voxel-based morphometry data.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: pathology,Brain: physiopathology,Cognition Disorders,Cognition Disorders: genetics,Cognition Disorders: pathology,False Positive Reactions,Genotype,Humans,Magnetic Resonance Imaging,Polymorphism, Single Nucleotide},
month = {jan},
number = {2},
pages = {992--1000},
pmid = {20849959},
publisher = {Elsevier Inc.},
title = {{False positives in neuroimaging genetics using voxel-based morphometry data.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3063336{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {54},
year = {2011}
}
@article{Marroquin2011,
abstract = {A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.},
author = {Marroquin, Jose L and Biscay, Rolando J and Ruiz-Correa, Salvador and Alba, Alfonso and Ramirez, Roxana and Armony, Jorge L},
doi = {10.1016/j.neuroimage.2011.03.081},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Marroquin et al. - 2011 - Morphology-based hypothesis testing in discrete random fields a non-parametric method to address the multiple-.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain: anatomy {\&} histology,Brain: physiology,Electroencephalography,Evoked Potentials,Evoked Potentials: physiology,Humans,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Magnetic Resonance Imaging,Signal Processing, Computer-Assisted,Statistics, Nonparametric},
month = {jun},
number = {4},
pages = {1954--67},
pmid = {21497660},
publisher = {Elsevier Inc.},
title = {{Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21497660},
volume = {56},
year = {2011}
}
@article{McKeown2003,
author = {McKeown, M},
doi = {10.1016/j.conb.2003.09.012},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/McKeown - 2003 - Independent component analysis of functional MRI what is signal and what is noise.pdf:pdf},
issn = {09594388},
journal = {Current Opinion in Neurobiology},
month = {oct},
number = {5},
pages = {620--629},
title = {{Independent component analysis of functional MRI: what is signal and what is noise?}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0959438803001338},
volume = {13},
year = {2003}
}
@article{Kelly2010,
abstract = {Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to motion and physiological sources, negatively impact the functional signal-to-noise ratio in fMRI studies, even after conventional fMRI preprocessing. Independent component analysis' demonstrated capacity to separate sources of neural signal, structured noise, and random noise into separate components might be utilized in improved procedures to remove artifacts from fMRI data. Such procedures require a method for labeling independent components (ICs) as representing artifacts to be removed or neural signals of interest to be spared. Visual inspection is often considered an accurate method for such labeling as well as a standard to which automated labeling methods are compared. However, detailed descriptions of methods for visual inspection of ICs are lacking in the literature. Here we describe the details of, and the rationale for, an operationalized fMRI data denoising procedure that involves visual inspection of ICs (96{\%} inter-rater agreement). We estimate that dozens of subjects/sessions can be processed within a few hours using the described method of visual inspection. Our hope is that continued scientific discussion of and testing of visual inspection methods will lead to the development of improved, cost-effective fMRI denoising procedures.},
author = {Kelly, Robert E and Alexopoulos, George S and Wang, Zhishun and Gunning, Faith M and Murphy, Christopher F and Morimoto, Sarah Shizuko and Kanellopoulos, Dora and Jia, Zhiru and Lim, Kelvin O and Hoptman, Matthew J},
doi = {10.1016/j.jneumeth.2010.03.028},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Kelly et al. - 2010 - Visual inspection of independent components defining a procedure for artifact removal from fMRI data.pdf:pdf},
issn = {1872-678X},
journal = {Journal of neuroscience methods},
keywords = {Artifacts,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Computer Simulation,Data Interpretation, Statistical,Databases as Topic,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Mental Processes,Mental Processes: physiology,Middle Aged,Neuropsychological Tests,Observer Variation,Rest,Time Factors,Visual Perception,Visual Perception: physiology},
month = {jun},
number = {2},
pages = {233--45},
pmid = {20381530},
publisher = {Elsevier B.V.},
title = {{Visual inspection of independent components: defining a procedure for artifact removal from fMRI data.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3299198{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {189},
year = {2010}
}
@article{Meehl1978,
author = {Meehl, Paul E},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Meehl - 1978 - Theoretical Risks and Tabular Asterisks Sir Karl, Sir Ronald, and the Slow Progress of Soft Psychology.pdf:pdf},
journal = {Journal of Consulting an Clinical Psychology},
pages = {806--834},
title = {{Theoretical Risks and Tabular Asterisks: Sir Karl, Sir Ronald, and the Slow Progress of Soft Psychology}},
volume = {46},
year = {1978}
}
@article{Schmidt2002,
author = {Schmidt, F. and Hunter, J.},
journal = {American Psychologist},
number = {1},
pages = {65--66},
title = {{Are there benefits from NHST}},
volume = {57},
year = {2002}
}
@article{Richardson2004,
abstract = {Functional MRI (fMRI) of cognitive tasks depends on technology widely available in the clinical sphere, but has yet to show a role in the investigation of patients. We report here the first demonstration of a clinically valuable role for cognitive fMRI. Temporal lobe epilepsy (TLE) is commonly caused by hippocampal sclerosis and is frequently resistant to drug treatment. Surgical resection of the left hippocampus in this setting can cure seizures, but may produce significant verbal memory decline, which is hard to predict. We report 10 right-handed TLE patients with left hippocampal sclerosis who underwent left hippocampal resection. We compared currently used data for the prediction of post-operative verbal memory decline in such patients with a novel fMRI assessment of verbal memory encoding. Multiple regression analyses showed that fMRI provided the strongest independent predictor of memory outcome after surgery. At the individual subject level, the fMRI data had high positive predictive value for memory decline.},
author = {Richardson, Mark P and Strange, Bryan a and Thompson, Pamela J and Baxendale, Sallie a and Duncan, John S and Dolan, Raymond J},
doi = {10.1093/brain/awh293},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Richardson et al. - 2004 - Pre-operative verbal memory fMRI predicts post-operative memory decline after left temporal lobe resection(2).pdf:pdf},
issn = {1460-2156},
journal = {Brain : a journal of neurology},
keywords = {Adult,Anterior Temporal Lobectomy,Anterior Temporal Lobectomy: adverse effects,Epilepsy, Temporal Lobe,Epilepsy, Temporal Lobe: etiology,Epilepsy, Temporal Lobe: psychology,Epilepsy, Temporal Lobe: surgery,Female,Hippocampus,Hippocampus: pathology,Hippocampus: surgery,Humans,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Memory,Memory Disorders,Memory Disorders: etiology,Middle Aged,Neuropsychological Tests,Preoperative Care,Prognosis,Prospective Studies,Sclerosis},
month = {nov},
number = {Pt 11},
pages = {2419--26},
pmid = {15459025},
title = {{Pre-operative verbal memory fMRI predicts post-operative memory decline after left temporal lobe resection.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15459025},
volume = {127},
year = {2004}
}
@article{Pillai2010,
abstract = {BOLD fMRI has, during the past decade, made a major transition from a purely research imaging technique to a viable clinical technique used primarily for presurgical planning in patients with brain tumors and other resectable brain lesions. This review article briefly examines the history and evolution of clinical functional imaging, with particular emphasis on how the use of BOLD fMRI for neurosurgical planning has changed during the past 2 decades. Even more important, this article describes the many published studies during that same period that have examined the overall clinical impact that BOLD and DTI have made on surgical planning.},
author = {Pillai, J J},
doi = {10.3174/ajnr.A1845},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Pillai - 2010 - The evolution of clinical functional imaging during the past 2 decades and its current impact on neurosurgical planning.pdf:pdf},
issn = {1936-959X},
journal = {AJNR. American journal of neuroradiology},
keywords = {Brain Neoplasms,Brain Neoplasms: diagnosis,Brain Neoplasms: history,Brain Neoplasms: surgery,History, 20th Century,History, 21st Century,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: history,Neuroradiography,Neuroradiography: history,Neurosurgical Procedures,Neurosurgical Procedures: history},
month = {feb},
number = {2},
pages = {219--25},
pmid = {20150316},
title = {{The evolution of clinical functional imaging during the past 2 decades and its current impact on neurosurgical planning.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20150316},
volume = {31},
year = {2010}
}
@article{Duncan2010,
abstract = {Medically refractory focal epilepsy is potentially curable by surgery. This Review considers the application of recent advances in structural and functional brain imaging to increase the number of patients with epilepsy who are treated surgically, and to reduce the risk of complications arising from such intervention. Current optimal MRI of brain structure can identify previously undetectable lesions, with voxel-based and quantitative analyses further increasing the diagnostic yield. If MRI proves unremarkable, PET (with (18)F-fluorodeoxyglucose) and single-photon emission CT of ictal-interictal cerebral blood flow might identify the brain region that contains the epileptic focus. Magnetoencephalography plus simultaneous EEG and functional MRI can map the location of interictal epileptic discharges, thereby facilitating placement of intracranial recording electrodes to define the site of seizure onset. Functional MRI can also lateralize language and localize primary motor, somatosensory and language areas, and shows promise for predicting the effects of temporal lobe resection on memory. Tractography can visualize the main cerebral white matter tracts, thereby predicting and reducing surgery risk. Currently, displays of the optic radiation and pyramidal tracts are the most relevant for epilepsy surgery. Reliable integration of structural and functional data into surgical image-guidance systems is being pursued, and promises safer neurosurgery for epilepsy in the future.},
author = {Duncan, John S},
doi = {10.1038/nrneurol.2010.131},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Duncan - 2010 - Imaging in the surgical treatment of epilepsy.pdf:pdf},
issn = {1759-4766},
journal = {Nature reviews. Neurology},
keywords = {Brain,Brain Mapping,Brain: pathology,Brain: radiography,Brain: radionuclide imaging,Diagnostic Imaging,Diagnostic Imaging: classification,Diagnostic Imaging: methods,Epilepsy,Epilepsy: diagnosis,Epilepsy: etiology,Epilepsy: surgery,Humans},
month = {oct},
number = {10},
pages = {537--50},
pmid = {20842185},
publisher = {Nature Publishing Group},
title = {{Imaging in the surgical treatment of epilepsy.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20842185},
volume = {6},
year = {2010}
}
@article{Binder2011,
abstract = {Partial removal of the anterior temporal lobe (ATL) is a highly effective surgical treatment for intractable temporal lobe epilepsy, yet roughly half of patients who undergo left ATL resection show a decline in language or verbal memory function postoperatively. Two recent studies demonstrate that preoperative fMRI can predict postoperative naming and verbal memory changes in such patients. Most importantly, fMRI significantly improves the accuracy of prediction relative to other noninvasive measures used alone. Addition of language and memory lateralization data from the intracarotid amobarbital (Wada) test did not improve prediction accuracy in these studies. Thus, fMRI provides patients and practitioners with a safe, noninvasive, and well-validated tool for making better-informed decisions regarding elective surgery based on a quantitative assessment of cognitive risk.},
author = {Binder, Jeffrey R},
doi = {10.1016/j.yebeh.2010.08.004},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Binder - 2011 - Functional MRI is a valid noninvasive alternative to Wada testing.pdf:pdf},
issn = {1525-5069},
journal = {Epilepsy {\&} behavior : E{\&}B},
keywords = {Brain,Brain: blood supply,Brain: physiopathology,Epilepsy, Temporal Lobe,Epilepsy, Temporal Lobe: physiopathology,Epilepsy, Temporal Lobe: surgery,Functional Laterality,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Memory,Memory: physiology,Oxygen,Oxygen: blood,Predictive Value of Tests,Verbal Learning,Verbal Learning: physiology},
month = {feb},
number = {2},
pages = {214--22},
pmid = {20850386},
publisher = {Elsevier Inc.},
title = {{Functional MRI is a valid noninvasive alternative to Wada testing.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3008762{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {20},
year = {2011}
}
@article{Vul2009,
author = {Vul, Edward and Harris, Christine and Winkielman, Piotr and Pashler, Harold},
doi = {10.1111/j.1745-6924.2009.01125.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Vul et al. - 2009 - Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition.pdf:pdf},
issn = {17456916},
journal = {Perspectives on Psychological Science},
month = {may},
number = {3},
pages = {274--290},
title = {{Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition}},
url = {http://pps.sagepub.com/lookup/doi/10.1111/j.1745-6924.2009.01125.x},
volume = {4},
year = {2009}
}
@article{Beckmann2004,
author = {Beckmann, Christian F and Smith, Stephen M},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Beckmann, Smith - 2004 - Probabilistic independent component analysis for functional magnetic resonance imaging.pdf:pdf},
journal = {IEEE transactions on medical imaging},
number = {2},
pages = {137--152},
title = {{Probabilistic independent component analysis for functional magnetic resonance imaging}},
volume = {23},
year = {2004}
}
@article{Smith2004,
abstract = {The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).},
author = {Smith, Stephen M and Jenkinson, Mark and Woolrich, Mark W and Beckmann, Christian F and Behrens, Timothy E J and Johansen-Berg, Heidi and Bannister, Peter R and {De Luca}, Marilena and Drobnjak, Ivana and Flitney, David E and Niazy, Rami K and Saunders, James and Vickers, John and Zhang, Yongyue and {De Stefano}, Nicola and Brady, J Michael and Matthews, Paul M},
doi = {10.1016/j.neuroimage.2004.07.051},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Smith et al. - 2004 - Advances in functional and structural MR image analysis and implementation as FSL.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Bayes Theorem,Brain,Brain: anatomy {\&} histology,Brain: physiology,Computer-Assisted,Databases,Factual,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Models,Neurological,Software,Statistical},
month = {jan},
pages = {S208--19},
pmid = {15501092},
title = {{Advances in functional and structural MR image analysis and implementation as FSL.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15501092},
volume = {23 Suppl 1},
year = {2004}
}
@article{Spearman1904,
author = {Spearman, C.},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Spearman - 1904 - The Proof and Measurement of Association between Two Things.pdf:pdf},
journal = {The American Journal of Psychology},
number = {1},
pages = {72--101},
title = {{The Proof and Measurement of Association between Two Things}},
volume = {15},
year = {1904}
}
@article{Esposito2005a,
abstract = {Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple subjects into a single aggregate data set before ICA estimation and, thus, require some additional assumptions about the separability across subjects of group independent components. Here, we exploit the application of similarity measures and a related visual tool to study the natural self-organizing clustering of many independent components from multiple individual data sets in the subject space. Our proposed framework flexibly enables multiple criteria for the generation of group independent components and their random-effects evaluation. We present real visual activation fMRI data from two experiments, with different spatiotemporal structures, and demonstrate the validity of this framework for a blind extraction and selection of meaningful activity and functional connectivity group patterns. Our approach is either alternative or complementary to the group ICA of aggregated data sets in that it exploits commonalities across multiple subject-specific patterns, while addressing as much as possible of the intersubject variability of the measured responses. This property is particularly of interest for a blind group and subgroup pattern extraction and selection.},
author = {Esposito, Fabrizio and Scarabino, Tommaso and Hyvarinen, Aapo and Himberg, Johan and Formisano, Elia and Comani, Silvia and Tedeschi, Gioacchino and Goebel, Rainer and Seifritz, Erich and {Di Salle}, Francesco},
doi = {10.1016/j.neuroimage.2004.10.042},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Esposito et al. - 2005 - Independent component analysis of fMRI group studies by self-organizing clustering.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Brain Mapping,Cluster Analysis,Dominance, Cerebral,Dominance, Cerebral: physiology,Female,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: statistics {\&},Imaging, Three-Dimensional,Imaging, Three-Dimensional: statistics {\&} numerical,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Male,Numerical Analysis, Computer-Assisted,Pattern Recognition, Visual,Pattern Recognition, Visual: physiology,Photic Stimulation,Principal Component Analysis,Reference Values,Reproducibility of Results,Visual Cortex,Visual Cortex: physiology},
month = {mar},
number = {1},
pages = {193--205},
pmid = {15734355},
title = {{Independent component analysis of fMRI group studies by self-organizing clustering.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15734355},
volume = {25},
year = {2005}
}
@article{Hayasaka2003,
author = {Hayasaka, Satoru},
doi = {10.1016/j.neuroimage.2003.08.003},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hayasaka - 2003 - Validating cluster size inference random field and permutation methods.pdf:pdf},
isbn = {1734763221},
issn = {10538119},
journal = {NeuroImage},
month = {dec},
number = {4},
pages = {2343--2356},
title = {{Validating cluster size inference: random field and permutation methods}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811903005020},
volume = {20},
year = {2003}
}
@article{,
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Unknown - Unknown - Post-hoc power estimation for topological inference in fMRI.pdf:pdf},
title = {{Post-hoc power estimation for topological inference in fMRI}}
}
@incollection{Worsley2007,
abstract = {Random field theory is used in the statistical analysis of statistical parametric maps (SPMs) whenever there is a spatial component to the inference. Most important is the question of detecting an effect or activation at an unknown spatial location. Very often we do not know in advance where to look for an effect, and we are interested in searching the whole brain, or part of it. This chapter presents special statistical problems related to the problem of multiple comparisons, or multiple tests. Two methods have been proposed, the first based on the maximum of the T or F statistic, the second based on the spatial extent of the region where these statistics exceed some threshold value. Both involve results from random field theory (Adler, 1981).},
address = {London},
author = {Worsley, Keith J},
booktitle = {Statistical Parametric Mapping},
chapter = {Random Fie},
doi = {10.1016/B978-012372560-8/50018-8},
editor = {Friston, K.J. and Ashburner, J. and Kiebel, S. J. and Nichols, Thomas E. and Penny, W.},
pages = {232--236},
publisher = {Academic Press},
title = {{Random Field Theory}},
url = {http://www.sciencedirect.com/science/article/pii},
year = {2007}
}
@article{Johnson2012,
abstract = {The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this article, we present a non-parametric Potts model and apply it to a functional magnetic resonance imaging study for the pre-surgical assessment of peritumoral brain activation. In our model, we assume that the Z-score image from a patient can be segmented into activated, deactivated, and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm, and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belongs to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified and outperforms parametric models when the parametric model in misspecified.},
author = {Johnson, Timothy D and Liu, Zhuqing and Bartsch, Andreas Joachim and Nichols, Thomas E},
doi = {10.1177/0962280212448970},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Johnson et al. - 2012 - A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.pdf:pdf},
issn = {1477-0334},
journal = {Statistical methods in medical research},
month = {may},
pmid = {22627277},
title = {{A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22627277},
year = {2012}
}
@article{Efron2002,
author = {Efron, Bradley and Tibshirani, Robert},
doi = {10.1002/gepi.01124},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron, Tibshirani - 2002 - Empirical Bayes Methods and False Discovery Rates for Microarrays.pdf:pdf},
keywords = {a posteriori probability of,gene,multiple comparisons,simultaneous hypothesis tests},
number = {March},
pages = {70--86},
title = {{Empirical Bayes Methods and False Discovery Rates for Microarrays}},
volume = {86},
year = {2002}
}
@unpublished{Efron2005,
author = {Efron, Bradley},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron - 2005 - Local False Discovery Rates.pdf:pdf},
institution = {Stanford University},
pages = {29},
series = {Division of Biostatistics Technical Reports},
title = {{Local False Discovery Rates}},
year = {2005}
}
@article{Efron2010,
abstract = {We consider large-scale studies in which there are hundreds or thousands of correlated cases to investigate, each represented by its own normal variate, typically a z-value. A familiar example is provided by a microarray experiment comparing healthy with sick subjects' expression levels for thousands of genes. This paper concerns the accuracy of summary statistics for the collection of normal variates, such as their empirical cdf or a false discovery rate statistic. It seems like we must estimate an N by N correlation matrix, N the number of cases, but our main result shows that this is not necessary: good accuracy approximations can be based on the root mean square correlation over all N {\textperiodcentered} (N - 1)/2 pairs, a quantity often easily estimated. A second result shows that z-values closely follow normal distributions even under non-null conditions, supporting application of the main theorem. Practical application of the theory is illustrated for a large leukemia microarray study.},
author = {Efron, Bradley},
doi = {10.1198/jasa.2010.tm09129},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron - 2010 - Correlated z-values and the accuracy of large-scale statistical estimates.pdf:pdf},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {Mehler's identity,acceleration,correlation penalty,empirical process,mehler,nonnull z-values,rms correlation,s identity},
month = {sep},
number = {491},
pages = {1042--1055},
pmid = {21052523},
title = {{Correlated z-values and the accuracy of large-scale statistical estimates.}},
volume = {105},
year = {2010}
}
@article{Efron2004,
author = {Efron, Bradley},
doi = {10.1198/016214504000000089},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron - 2004 - Large-Scale Simultaneous Hypothesis Testing The Choice of a Null Hypothesis.pdf:pdf},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {empirical bayes,empirical null hypothesis,local false discovery rate,microarray analysis,unobserved covariates},
month = {mar},
number = {465},
pages = {96--104},
title = {{Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis}},
volume = {99},
year = {2004}
}
@article{Efron2007,
author = {Efron, Bradley},
doi = {10.1198/016214506000001211},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron - 2007 - Correlation and Large-Scale Simultaneous Significance Testing.pdf:pdf},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {correlated processes,empirical null,false discovery rate,microarray},
month = {mar},
number = {477},
pages = {93--103},
title = {{Correlation and Large-Scale Simultaneous Significance Testing}},
volume = {102},
year = {2007}
}
@article{Gorgolewski2012,
author = {Gorgolewski, Krzysztof J. and Storkey, Amos J. and Bastin, Mark E. and Pernet, Cyril R.},
doi = {10.3389/fnhum.2012.00245},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Gorgolewski et al. - 2012 - Adaptive thresholding for reliable topological inference in single subject fMRI analysis.pdf:pdf},
issn = {1662-5161},
journal = {Frontiers in Human Neuroscience},
keywords = {false negative errors,mixture models,mixture models, random field theory, false negativ,random field theory,reliability,spatial accuracy},
number = {August},
pages = {1--14},
title = {{Adaptive thresholding for reliable topological inference in single subject fMRI analysis}},
url = {http://www.frontiersin.org/Human{\_}Neuroscience/10.3389/fnhum.2012.00245/abstract},
volume = {6},
year = {2012}
}
@article{Hayasaka2004b,
abstract = {In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and extent information needs to be combined. Various ways of combining voxel intensity and cluster extent are possible, and a few such combining methods have been proposed. Poline et al.'s [NeuroImage 16 (1997) 83] minimum P value approach is sensitive to signals whose either intensity or extent is significant. Bullmore et al.'s [IEEE Trans. Med. Imag. 18 (1999) 32] cluster mass method can detect signals whose intensity and extent are sufficiently large, even when they are not significant by intensity or extent alone. In this work, we study such combined inference methods using combining functions (Pesarin, F., 2001. Multivariate Permutation Tests. Wiley, New York) and permutation framework [Holmes et al., J. Cereb. Blood Flow Metab. 16 (1996) 7], which allow us to examine different ways of combining voxel intensity and cluster extent information without knowing their distribution. We also attempt to calibrate combined inference by using weighted combining functions, which adjust the test according to signals of interest. Furthermore, we propose meta-combining, a combining function of combining functions, which integrates strengths of multiple combining functions into a single statistic. We found that combined tests are able to detect signals that are not detected by voxel or cluster size test alone. We also found that the weighted combining functions can calibrate the combined test according to the signals of interest, emphasizing either intensity or extent as appropriate. Though not necessarily more sensitive than individual combining functions, the meta-combining function is sensitive to all types of signals and thus can be used as a single test summarizing all the combining functions.},
author = {Hayasaka, Satoru and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2004.04.035},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hayasaka, Nichols - 2004 - Combining voxel intensity and cluster extent with permutation test framework.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Artifacts,Brain,Brain Mapping,Brain: pathology,Brain: physiopathology,Cerebral Cortex,Cerebral Cortex: pathology,Cerebral Cortex: physiopathology,Cluster Analysis,Computer Simulation,Emotions,Emotions: physiology,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: statistics {\&},Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Mathematical Computing,Memory, Short-Term,Memory, Short-Term: physiology,Sensitivity and Specificity},
month = {sep},
number = {1},
pages = {54--63},
pmid = {15325352},
title = {{Combining voxel intensity and cluster extent with permutation test framework.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15325352},
volume = {23},
year = {2004}
}
@article{Wilke2012a,
abstract = {For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups ({\textless}20-27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a "dual-use" fMRI task, n = 39), and the implications of this approach are discussed.},
author = {Wilke, Marko},
doi = {10.1371/journal.pone.0035578},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wilke - 2012 - An iterative jackknife approach for assessing reliability and power of FMRI group analyses.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
month = {jan},
number = {4},
pmid = {22530053},
title = {{An iterative jackknife approach for assessing reliability and power of FMRI group analyses.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3328456{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2012}
}
@article{Suckling2010a,
abstract = {Magnetic resonance imaging (MRI) is widely used in brain imaging research (neuroimaging) to explore structural and functional changes across dispersed neural networks visible only via multisubject experiments. Multicenter investigations are an effective way to increase recruitment rates. This article describes image-based power calculations for a two-group, cross-sectional design specified by the mean effect size and its standard error, sample size, false discovery rate (FDR), and size of the network (i.e., proportion of image locations) that truly demonstrates an effect. Minimum sample size (for fixed effect size) and the minimum effect size (for fixed sample size) are calculated by specifying the acceptable power threshold. Within-center variance was estimated in five participating centers by repeat MRI scanning of 12 healthy participants from whom distributions of gray matter were estimated. The effect on outcome measures when varying FDR and the proportion of true positives is presented. Their spatial patterns reflect within-center variance, which is consistent across centers. Sample sizes 3-6 times larger are needed when detecting effects in subcortical regions compared to the neocortex. Hypothesized multicenter studies of patients with first episode psychosis and control participants were simulated with varying proportions of the cohort recruited at each center. There is little penalty to sample size for recruitment at five centers compared to the center with the lowest variance alone. At 80{\%} power 80 participants per group are required to observe differences in gray matter in high variance regions.},
author = {Suckling, John and Barnes, Anna and Job, Dominic and Brenan, David and Lymer, Katherine and Dazzan, Paola and Marques, Tiago Reis and MacKay, Clare and McKie, Shane and Williams, Steve R and Williams, Steven C R and Lawrie, Stephen and Deakin, Bill},
doi = {10.1002/hbm.20927},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Suckling et al. - 2010 - Power calculations for multicenter imaging studies controlled by the false discovery rate.pdf:pdf},
issn = {1097-0193},
journal = {Human brain mapping},
keywords = {Brain,Brain Mapping,Brain: anatomy {\&} histology,Computer Simulation,Female,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Male,Models, Statistical,Statistics as Topic},
month = {aug},
number = {8},
pages = {1183--95},
pmid = {20063303},
title = {{Power calculations for multicenter imaging studies controlled by the false discovery rate.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20063303},
volume = {31},
year = {2010}
}
@article{Schwartzman2011,
author = {Schwartzman, Armin and Gavrilov, Yulia and Adler, Robert J.},
doi = {10.1214/11-AOS943},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Schwartzman, Gavrilov, Adler - 2011 - Multiple testing of local maxima for detection of peaks in 1D.pdf:pdf},
issn = {0090-5364},
journal = {The Annals of Statistics},
month = {dec},
number = {6},
pages = {3290--3319},
title = {{Multiple testing of local maxima for detection of peaks in 1D}},
volume = {39},
year = {2011}
}
@article{Nichols2002a,
author = {Nichols, Thomas E and Arbor, Ann},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nichols, Arbor - 2002 - Visualizing Variance with Percent Change Threshold.pdf:pdf},
keywords = {confidence intervals,global estimation,mode estimation,statisti-,statistical inference},
pages = {1--21},
title = {{Visualizing Variance with Percent Change Threshold}},
year = {2002}
}
@article{Hayasaka2004a,
author = {Hayasaka, Satoru and Nichols, Thomas E},
journal = {Neuroimage},
pages = {54--63},
title = {{Combining voxel intesity and cluster extent with permutation test framework}},
volume = {23},
year = {2004}
}
@article{VanHorn1998,
abstract = {Using a classic technique based on the noncentral F-distribution method for computing statistical power, we developed a general approach to the estimation of voxel-based power in functional brain image data analysis. We applied this method to PET data from a large sample (N = 40) of subjects performing the Wisconsin Card Sorting (WCST) paradigm analyzed with SPM95, produced statistical power maps for a range of samples sizes and smoothing filter widths, and examined the effects of sample size and image smoothing on the expected reliability of activation findings. At an uncorrected alpha of 0.01, a fixed filter size of 10 mm3, and a range of power thresholds, maps revealed that the power to reject the null hypothesis in brain regions implicated in the task at Ns of 5 and 10 may not be sufficient to ensure reliable replication of significant findings and so should be interpreted with caution. At sample sizes approaching 20 subjects, sufficient power was found in the right dorsolateral prefrontal cortex (BA 46/9), right and left inferior parietal lobule (BA 40), and left inferior temporal lobe (BA 37), comprising the cortical network typically observed during the WCST. Filter size needed to maximize power varied widely, but systematically, across the brain, tending to follow known neuroanatomical landmarks. Statistical power considerations in brain imaging studies are critical for controlling the rate of false negatives and assuring reliable detection of cognitive activation. The variation of filter size for maximizing power across the brain suggests that the underlying neuroanatomy of functional units is an important consideration in the a priori selection of filter size.},
author = {{Van Horn}, J D and Ellmore, T M and Esposito, G and Berman, K F},
doi = {10.1006/nimg.1997.0317},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Van Horn et al. - 1998 - Mapping voxel-based statistical power on parametric images.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Algorithms,Brain Mapping,Cerebral Cortex,Cerebral Cortex: physiology,Cerebral Cortex: radionuclide imaging,Data Interpretation, Statistical,Humans,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Parietal Lobe,Parietal Lobe: physiology,Parietal Lobe: radionuclide imaging,Sample Size,Temporal Lobe,Temporal Lobe: physiology,Temporal Lobe: radionuclide imaging,Tomography, Emission-Computed},
month = {feb},
number = {2},
pages = {97--107},
pmid = {9558642},
title = {{Mapping voxel-based statistical power on parametric images.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9558642},
volume = {7},
year = {1998}
}
@misc{TheMendeleySupportTeam2011,
abstract = {A quick introduction to Mendeley. Learn how Mendeley creates your personal digital library, how to organize and annotate documents, how to collaborate and share with colleagues, and how to generate citations and bibliographies.},
address = {London},
author = {{The Mendeley Support Team}},
booktitle = {Mendeley Desktop},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/The Mendeley Support Team - 2011 - Getting Started with Mendeley.pdf:pdf},
keywords = {Mendeley,how-to,user manual},
pages = {1--16},
publisher = {Mendeley Ltd.},
title = {{Getting Started with Mendeley}},
url = {http://www.mendeley.com},
year = {2011}
}
@article{Hoenig2001,
author = {Hoenig, John M H and Eisey, Dennis M H},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hoenig, Eisey - 2001 - The Abuse of Power The Pervasive Fallacy of Power Calculations for Data Analysis.pdf:pdf},
journal = {The Americal Statistician},
keywords = {bioequivalence testing,burden of proof,observed power,power,retrospective power analysis,statistical,type ii error},
number = {1},
pages = {1--6},
title = {{The Abuse of Power : The Pervasive Fallacy of Power Calculations for Data Analysis}},
volume = {55},
year = {2001}
}
@article{Nichols2012,
author = {Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2012.04.014},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nichols - 2012 - Multiple testing corrections, nonparametric methods, and random field theory.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
month = {apr},
pages = {10--14},
publisher = {Elsevier Inc.},
title = {{Multiple testing corrections, nonparametric methods, and random field theory}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912003953},
year = {2012}
}
@article{Zarahn2001,
abstract = {In fMRI, the issues involved in the control of type I error are fairly well understood. In contrast, the control of type II error has received less formal attention. This is perhaps due to the fact that the consideration of type II error requires the specification of an alternative hypothesis/experimental effect. In this paper, we present a method for expressing experimental effects in fMRI in a manner relative to a reference effect. A reference effect is chosen based on its neurophysiological significance to the researcher. This method provides a means to quantitatively express alternative hypotheses for fMRI, thus allowing type II error assessment prior to the collection of fMRI data. The simultaneous control of both type I and type II error should make meaningful interpretations possible from both positive and negative fMRI results.},
author = {Zarahn, E and Slifstein, M},
doi = {10.1006/nimg.2001.0852},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zarahn, Slifstein - 2001 - A reference effect approach for power analysis in fMRI.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Brain,Brain: physiology,Humans,Least-Squares Analysis,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Neurological,Reference Values},
month = {sep},
number = {3},
pages = {768--79},
pmid = {11506549},
title = {{A reference effect approach for power analysis in fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11506549},
volume = {14},
year = {2001}
}
@article{Welvaert2011,
author = {Welvaert, Marijke and Durnez, Joke and Moerkerke, Beatrijs and Verdoolaege, Geert and Rosseel, Yves},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Welvaert et al. - 2011 - neuRosim An R package for generating fmri data.pdf:pdf},
journal = {Journal of Statistical Software},
keywords = {data generation,fmri,simulation},
number = {10},
pages = {1--18},
title = {{neuRosim: An R package for generating fmri data}},
volume = {44},
year = {2011}
}
@article{Bunch1978,
author = {Bunch, P. and Hamilton, J. and Sanderson, G. and Simmons, A},
journal = {Journal of Applied photographic engineering},
pages = {166--172},
title = {{A free response approach to the measurement and characterization of radiographic-observer performance}},
volume = {4},
year = {1978}
}
@book{Adler1981,
author = {Adler, Robert J.},
publisher = {Wiley, London},
title = {{The geometry of random fields}},
year = {1981}
}
@article{Bonferroni1936,
author = {Bonferroni, C.},
journal = {Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze},
pages = {3--62},
title = {{Teoria statistica delle classi e clcolo delle probabilita}},
volume = {8},
year = {1936}
}
@article{DeCharms2008,
abstract = {For centuries people have aspired to understand and control the functions of the mind and brain. It has now become possible to image the functioning of the human brain in real time using functional MRI (fMRI), and thereby to access both sides of the mind-brain interface--subjective experience (that is, one's mind) and objective observations (that is, external, quantitative measurements of one's brain activity)--simultaneously. Developments in neuroimaging are now being translated into many new potential practical applications, including the reading of brain states, brain-computer interfaces, communicating with locked-in patients, lie detection, and learning control over brain activation to modulate cognition or even treat disease.},
author = {DeCharms, R Christopher},
doi = {10.1038/nrn2414},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/deCharms - 2008 - Applications of real-time fMRI.pdf:pdf},
issn = {1471-003X},
journal = {Nature reviews. Neuroscience},
keywords = {Animals,Brain,Brain Mapping,Brain: blood supply,Brain: physiology,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Oxygen,Oxygen: blood,Psychophysiology,Time Factors},
month = {sep},
number = {9},
pages = {720--9},
pmid = {18714327},
title = {{Applications of real-time fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18714327},
volume = {9},
year = {2008}
}
@article{Dolan2008,
abstract = {Neuroimaging, particularly that based upon functional magnetic resonance (fMRI), has become a dominant tool in cognitive neuroscience. This review provides a personal and selective perspective on its past, present, and future. Two trends currently characterize the field that broadly reflect a pursuit of "where"- and "how"-type questions. The latter addresses basic mechanisms related to the expression of task-induced neural activity and is likely to be an increasingly important theme in the future. This trend entails an enhanced symbiosis among investigators pursuing similar questions in fields such as computational and theoretical neuroscience as well as through the detailed analysis of microcircuitry.},
author = {Dolan, R J},
doi = {10.1016/j.neuron.2008.10.038},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Dolan - 2008 - Neuroimaging of cognition past, present, and future.pdf:pdf},
issn = {1097-4199},
journal = {Neuron},
keywords = {Brain,Brain Mapping,Brain: anatomy {\&} histology,Brain: physiology,Cognition,Cognition: physiology,Diagnostic Imaging,Humans,Magnetic Resonance Imaging,Neurosciences},
month = {nov},
number = {3},
pages = {496--502},
pmid = {18995825},
publisher = {Elsevier Inc.},
title = {{Neuroimaging of cognition: past, present, and future.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2699840{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {60},
year = {2008}
}
@book{Lee2004,
author = {Lee, M.-L.},
publisher = {Kluwer,Boston},
title = {{Analysis of microarray gene expression data.}},
year = {2004}
}
@article{VanderLaan2004,
abstract = {This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise error rate (FWER) can be augmented into procedures that (asymptotically) control tail probabilities for the number of false positives and the proportion of false positives among the rejected hypotheses. Specifically, given any procedure that (asymptotically) controls the FWER at level alpha, we propose simple augmentation procedures that provide (asymptotic) level-alpha control of: (i) the generalized family-wise error rate, i.e., the tail probability, gFWER(k), that the number of Type I errors exceeds a user-supplied integer k, and (ii) the tail probability, TPPFP(q), that the proportion of Type I errors among the rejected hypotheses exceeds a user-supplied value 0{\textless}q{\textless}1. Existing approaches for control of the proportion of false positives typically rely on the assumption that the test statistics are independent, while our proposed augmentation procedures control the gFWER and TPPFP for general data generating distributions, with arbitrary dependence structures among variables. Applying the augmentation methods to step-down multiple testing procedures that control the FWER asymptotically exactly at level alpha (van der Laan et al., 2004), yields procedures that also provide exact asymptotic control of the gFWER and TPPFP at level alpha. The adjusted p-values for the gFWER and TPPFP-controlling augmentation procedures are shown to be simple functions of the adjusted p-values for the original FWER-controlling procedure. Finally, two simple conservative procedures are proposed for controlling the false discovery rate.},
author = {van der Laan, Mark J and Dudoit, Sandrine and Pollard, Katherine S},
doi = {10.2202/1544-6115.1042},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/van der Laan, Dudoit, Pollard - 2004 - Augmentation procedures for control of the generalized family-wise error rate and tail probabilit.pdf:pdf},
issn = {1544-6115},
journal = {Statistical applications in genetics and molecular biology},
month = {jan},
number = {1},
pages = {Article15},
pmid = {16646793},
title = {{Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16646793},
volume = {3},
year = {2004}
}
@article{Westfall1993,
author = {Westfall, Author P H and Young, S S and Wright, S Paul},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Westfall, Young, Wright - 1993 - On Adjusting P-Values for Multiplicity.pdf:pdf},
journal = {Biometrics},
number = {3},
pages = {941--945},
title = {{On Adjusting P-Values for Multiplicity}},
volume = {49},
year = {1993}
}
@article{Huettel2001,
abstract = {We comparedthe characteristics of the visually evoked hemodynamic response (HDR) in groups of young and elderly adults. Checkerboard stimuli were presented for 500 ms either singly or in pairs separated by a 2-s intrapair interval while gradient-echo echoplanar fMRI images were acquired concurrently every 1 s. Activated voxels, identified by correlation with an empirically derived reference waveform, were found for both groups in cortex along the calcarine sulcus and in the fusiform gyrus, with the mean HDR latency in calcarine cortex peaking approximately 300 ms earlier than the HDR evoked in the fusiform gyrus. On average, younger subjects had twice as many activated voxels as older subjects. The mean HDR had a similar onset time, rate of rise, and peak amplitude in both groups. However, the HDRs of older subjects reached their peak earlier and were more variable across subjects. Despite having average HDR amplitudes similar to those of younger subjects, older subjects had higher noise levels in activated voxels, resulting in lower signal-to-noise ratios. Distribution analyses of voxel statistics (t value, peak amplitude, peak latency) revealed that older subjects had proportionally fewer small-effect-size voxels, due to their increased voxelwise noise. This finding was consistent with the smaller spatial extent of activation in older subjects. To investigate age differences in the refractory period of the visual HDR, the HDR evoked by the second stimulus of each pair was isolated by subtracting the HDR evoked by a single stimulus from the composite HDR evoked by a pair. Recovery measures were similar across the age groups.},
author = {Huettel, S a and Singerman, J D and McCarthy, G},
doi = {10.1006/nimg.2000.0675},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Huettel, Singerman, McCarthy - 2001 - The effects of aging upon the hemodynamic response measured by functional MRI.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adolescent,Adult,Aged,Aging,Aging: physiology,Cerebrovascular Circulation,Cerebrovascular Circulation: physiology,Evoked Potentials, Visual,Evoked Potentials, Visual: physiology,Female,Head Movements,Head Movements: physiology,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Male,Middle Aged,Pattern Recognition, Visual,Pattern Recognition, Visual: physiology,Photic Stimulation},
month = {jan},
number = {1},
pages = {161--75},
pmid = {11133319},
title = {{The effects of aging upon the hemodynamic response measured by functional MRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11133319},
volume = {13},
year = {2001}
}
@article{DeGreck2011,
abstract = {Interdependent cultures (such as the Chinese) and independent cultures (such as the German) differ in their attitude towards harmony that is more valued in interdependent cultures. Interdependent and independent cultures also differ in their appreciation of anger - an emotion that implies the disruption of harmony. The present study investigated if interdependent and independent cultures foster distinct brain activity associated with empathic processing of familiar angry, familiar neutral, and unfamiliar neutral faces. Using functional MRI, we scanned Chinese and German healthy subjects during an intentional empathy task, a control task (the evaluation of skin color), and a baseline condition. The subject groups were matched with regard to age, gender, and education. Behaviorally, Chinese subjects described themselves as significantly more interdependent compared to German subjects. The contrast 'intentional empathy for familiar angry'{\textgreater}'baseline' revealed several regions, including the left inferior frontal cortex, the left supplementary motor area, and the left insula, that showed comparable hemodynamic responses in both groups. However, the left dorsolateral prefrontal cortex had stronger hemodynamic responses in Chinese subjects in the contrast 'intentional empathy for familiar angry'{\textgreater}'baseline'. Germans, in contrast, showed stronger hemodynamic responses in the right temporo-parietal junction, right inferior and superior temporal gyrus, and left middle insula for the same contrast. Hemodynamic responses in the latter three brain regions correlated with interdependences scores over all subjects. Our results suggest that enhanced emotion regulation during empathy with anger in the interdependent lifestyle is mediated by the left dorsolateral prefrontal cortex. Increased tolerance towards the expression of anger in the independent lifestyle, in contrast, is associated with increased activity of the right inferior and superior temporal gyrus and the left middle insula.},
author = {de Greck, Moritz and Shi, Zhenhao and Wang, Gang and Zuo, Xiangyu and Yang, Xuedong and Wang, Xiaoying and Northoff, Georg and Han, Shihui},
doi = {10.1016/j.neuroimage.2011.09.052},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/de Greck et al. - 2011 - Culture modulates brain activity during empathy with anger.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Anger,Emotion,Empathy,Transcultural,fMRI},
month = {sep},
number = {3},
pages = {2871--2882},
pmid = {21983057},
publisher = {Elsevier Inc.},
title = {{Culture modulates brain activity during empathy with anger.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21983057},
volume = {59},
year = {2011}
}
@article{Grinband2011,
author = {Grinband, Jack and Savitskaya, Judith and Wager, Tor D. and Teichert, Tobias and Ferrera, Vincent P. and Hirsch, Joy},
doi = {10.1016/j.neuroimage.2010.12.027},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Grinband et al. - 2011 - The dorsal medial frontal cortex is sensitive to time on task, not response conflict or error likelihood.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
month = {jul},
number = {2},
pages = {303--311},
publisher = {Elsevier Inc.},
title = {{The dorsal medial frontal cortex is sensitive to time on task, not response conflict or error likelihood}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811910016101},
volume = {57},
year = {2011}
}
@article{Zhang2011,
archivePrefix = {arXiv},
arxivId = {arXiv:1103.1966v1},
author = {Zhang, Chunming and Fan, Jianqing and Yu, Tao},
doi = {10.1214/10-AOS848},
eprint = {arXiv:1103.1966v1},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zhang, Fan, Yu - 2011 - Multiple testing via FDR L for large-scale imaging data.pdf:pdf},
issn = {0090-5364},
journal = {The Annals of Statistics},
keywords = {62H35, 62G10, 62P10, 62E20, Brain fMRI, false disc},
month = {feb},
number = {1},
pages = {613--642},
title = {{Multiple testing via FDR L for large-scale imaging data}},
url = {http://projecteuclid.org/euclid.aos/1297779858},
volume = {39},
year = {2011}
}
@article{Korn2004,
author = {Korn, E and Troendle, J and Mcshane, L and Simon, R},
doi = {10.1016/S0378-3758(03)00211-8},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Korn et al. - 2004 - Controlling the number of false discoveries application to high-dimensional genomic data.pdf:pdf},
issn = {03783758},
journal = {Journal of Statistical Planning and Inference},
keywords = {expression,false discovery,gene,microarray,multiple comparisons,permutation method,stepwise procedure},
month = {sep},
number = {2},
pages = {379--398},
title = {{Controlling the number of false discoveries: application to high-dimensional genomic data}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0378375803002118},
volume = {124},
year = {2004}
}
@book{Lahiri2003,
author = {Lahiri, S.N.},
keywords = {resampling},
mendeley-tags = {resampling},
publisher = {Springer},
title = {{Resampling methods for dependent data}},
year = {2003}
}
@article{Zarahn2002,
author = {Zarahn, E. and Friston, Karl J},
doi = {10.1002/1521-4036(200206)44:4<496::AID-BIMJ496>3.0.CO;2-5},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zarahn, Friston - 2002 - Some Limit Results for Efficiency in Stochastic fMRI Designs.pdf:pdf},
issn = {03233847},
journal = {Biometrical Journal},
keywords = {efficiency,fmri,stochastic design},
month = {jun},
number = {4},
pages = {496},
title = {{Some Limit Results for Efficiency in Stochastic fMRI Designs}},
url = {http://doi.wiley.com/10.1002/1521-4036(200206)44:4{\%}3C496::AID-BIMJ496{\%}3E3.0.CO;2-5},
volume = {44},
year = {2002}
}
@article{Gordon2007,
archivePrefix = {arXiv},
arxivId = {arXiv:0709.0366v1},
author = {Gordon, Alexander and Glazko, Galina and Qiu, Xing and Yakovlev, Andrei},
doi = {10.1214/07-AOAS102},
eprint = {arXiv:0709.0366v1},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Gordon et al. - 2007 - Control of the mean number of false discoveries, Bonferroni and stability of multiple testing.pdf:pdf},
issn = {1932-6157},
journal = {Annals of Applied Statistics},
month = {jun},
number = {1},
pages = {179--190},
title = {{Control of the mean number of false discoveries, Bonferroni and stability of multiple testing}},
url = {http://projecteuclid.org/euclid.aoas/1183143734},
volume = {1},
year = {2007}
}
@article{Qiu2006,
abstract = {The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data.},
author = {Qiu, Xing and Xiao, Yuanhui and Gordon, Alexander and Yakovlev, Andrei},
doi = {10.1186/1471-2105-7-50},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Qiu et al. - 2006 - Assessing stability of gene selection in microarray data analysis.pdf:pdf},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Child,Computer Simulation,Data Interpretation, Statistical,Gene Expression Profiling,Gene Expression Profiling: methods,Genetic Variation,Humans,Leukemia,Leukemia: genetics,Models, Genetic,Models, Statistical,Neoplasm Proteins,Neoplasm Proteins: genetics,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Reproducibility of Results,Sensitivity and Specificity,Tumor Markers, Biological,Tumor Markers, Biological: genetics},
month = {jan},
pages = {50},
pmid = {16451725},
title = {{Assessing stability of gene selection in microarray data analysis.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1403808{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2006}
}
@article{Gordon2009,
abstract = {A new procedure is proposed to balance type I and II errors in significance testing for differential expression of individual genes. Suppose that a collection, F(k), of k lists of selected genes is available, each of them approximating by their content the true set of differentially expressed genes. For example, such sets can be generated by a subsampling counterpart of the delete-d-jackknife method controlling the per-comparison error rate for each subsample. A final list of candidate genes, denoted by S(*), is composed in such a way that its contents be closest in some sense to all the sets thus generated. To measure "closeness" of gene lists, we introduce an asymmetric distance between sets with its asymmetry arising from a generally unequal assignment of the relative costs of type I and type II errors committed in the course of gene selection. The optimal set S(*) is defined as a minimizer of the average asymmetric distance from an arbitrary set S to all sets in the collection F(k). The minimization problem can be solved explicitly, leading to a frequency criterion for the inclusion of each gene in the final set. The proposed method is tested by resampling from real microarray gene expression data with artificially introduced shifts in expression levels of pre-defined genes, thereby mimicking their differential expression.},
author = {Gordon, Alexander and Chen, Linlin and Glazko, Galina and Yakovlev, Andrei},
doi = {10.1016/j.csda.2008.04.010},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Gordon et al. - 2009 - Balancing Type One and Two Errors in Multiple Testing for Differential Expression of Genes.pdf:pdf},
issn = {0167-9473},
journal = {Computational statistics {\&} data analysis},
keywords = {error rates,microarray data,multiple testing,resampling},
month = {mar},
number = {5},
pages = {1622--1629},
pmid = {20161303},
title = {{Balancing Type One and Two Errors in Multiple Testing for Differential Expression of Genes.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2699298{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {53},
year = {2009}
}
@article{Worsley2004,
abstract = {We report new random field theory P values for peaks of canonical correlation SPMs for detecting multiple contrasts in a linear model for multivariate image data. This completes results for all types of univariate and multivariate image data analysis. All other known univariate and multivariate random field theory results are now special cases, so these new results present a true unification of all currently known results. As an illustration, we use these results in a deformation-based morphometry (DBM) analysis to look for regions of the brain where vector deformations of nonmissile trauma patients are related to several verbal memory scores, to detect regions of changes in anatomical effective connectivity between the trauma patients and a group of age- and sex-matched controls, and to look for anatomical connectivity in cortical thickness.},
author = {Worsley, Keith J and Taylor, Jonathan E and Tomaiuolo, Francesco and Lerch, Jason},
doi = {10.1016/j.neuroimage.2004.07.026},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Worsley et al. - 2004 - Unified univariate and multivariate random field theory.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Algorithms,Analysis of Variance,Brain,Brain Injuries,Brain Injuries: pathology,Brain: anatomy {\&} histology,Brain: pathology,Cerebral Cortex,Cerebral Cortex: injuries,Cerebral Cortex: pathology,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: statistics {\&},Linear Models,Magnetic Resonance Imaging,Models, Statistical},
month = {jan},
pages = {S189--95},
pmid = {15501088},
title = {{Unified univariate and multivariate random field theory.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15501088},
volume = {23 Suppl 1},
year = {2004}
}
@book{Friston2007,
author = {Friston, Karl J and Ashburner, J and Kiebel, S. J. and Nichols, Thomas E and Penny, W. D.},
isbn = {0123725607},
keywords = {SPM},
title = {{Statistical parametric mapping: the analysis of functional brain images.}},
year = {2007}
}
@article{Chen2009,
abstract = {Voxel-based morphometry (VBM) is widely used as a high-resolution approach to understanding the relationship between anatomical structures and variables of interest. Controlling for the false discovery rate (FDR) is an attractive choice for thresholding the resulting statistical maps and has been commonly used in fMRI studies. However, we caution against the use of nonadaptive FDR control procedures, such as the most commonly used Benjamini-Hochberg procedure (B-H), in VBM analyses. This is because, in VBM analyses, specific risk factors may be associated with volume change in a global, rather than local, manner, which means the proportion of truly associated voxels among all voxels is large. In such a case, the achieved FDR obtained by nonadaptive procedures can be substantially lower than the nominal, or controlled, level. Such conservatism deprives researchers of power for detecting true associations. In this article, we advocate for the use of adaptive FDR control in VBM-type analyses. Specifically, we examine two representative adaptive procedures: the two-stage step-up procedure by Benjamini, Krieger and Yekutieli (2006: Biometrika 93:491-507) and the procedure of Storey and Tibshirani ([2003]: Proc Natl Acad Sci USA 100:9440-9445). We demonstrate mathematically, with simulations, and with a data example that these procedures provide improved performance over the B-H procedure.},
author = {Chen, Sining and Wang, Chi and Eberly, Lynn E and Caffo, Brian S and Schwartz, Brian S},
doi = {10.1002/hbm.20669},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Chen et al. - 2009 - Adaptive control of the false discovery rate in voxel-based morphometry.pdf:pdf},
issn = {1097-0193},
journal = {Human brain mapping},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Cognition,Computer Simulation,False Positive Reactions,Female,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Magnetic Resonance Imaging,Male,Middle Aged,Organ Size},
month = {jul},
number = {7},
pages = {2304--11},
pmid = {19034901},
title = {{Adaptive control of the false discovery rate in voxel-based morphometry.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19034901},
volume = {30},
year = {2009}
}
@article{Worsley1995,
author = {Worsley, K J},
doi = {10.2307/1427930},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Worsley - 1995 - Boundary Corrections for the Expected Euler Characteristic of Excursion Sets of Random Fields, with an Application to A.pdf:pdf},
issn = {00018678},
journal = {Advances in Applied Probability},
month = {dec},
number = {4},
pages = {943},
title = {{Boundary Corrections for the Expected Euler Characteristic of Excursion Sets of Random Fields, with an Application to Astrophysics}},
url = {http://www.jstor.org/stable/1427930?origin=crossref},
volume = {27},
year = {1995}
}
@article{Storey2001,
author = {Storey, John D},
doi = {10.1111/1467-9868.00346},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Storey - 2001 - A direct approach to false discovery rates.pdf:pdf},
issn = {13697412},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
month = {aug},
number = {3},
pages = {479--498},
title = {{A direct approach to false discovery rates}},
url = {http://doi.wiley.com/10.1111/1467-9868.00346},
volume = {64},
year = {2002}
}
@article{Pounds2004,
abstract = {Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values.},
author = {Pounds, Stan and Cheng, Cheng},
doi = {10.1093/bioinformatics/bth160},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Pounds, Cheng - 2004 - Improving false discovery rate estimation.pdf:pdf},
issn = {1367-4803},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Benchmarking,Benchmarking: methods,Computer Simulation,False Positive Reactions,Gene Expression Profiling,Gene Expression Profiling: methods,Gene Expression Profiling: standards,Models, Genetic,Models, Statistical,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Oligonucleotide Array Sequence Analysis: standards,Quality Control,Reproducibility of Results,Sensitivity and Specificity},
month = {jul},
number = {11},
pages = {1737--45},
pmid = {14988112},
title = {{Improving false discovery rate estimation.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/14988112},
volume = {20},
year = {2004}
}
@article{Zehetmayer2010,
abstract = {The statistical power or multiple Type II error rate in large-scale multiple testing problems as, for example, in gene expression microarray experiments, depends on typically unknown parameters and is therefore difficult to assess a priori. However, it has been suggested to estimate the multiple Type II error rate post hoc, based on the observed data.},
author = {Zehetmayer, Sonja and Posch, Martin},
doi = {10.1093/bioinformatics/btq085},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zehetmayer, Posch - 2010 - Post hoc power estimation in large-scale multiple testing problems.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Gene Expression Profiling,Gene Expression Profiling: methods,Genomics,Genomics: methods,Humans,Models, Statistical,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Tumor Markers, Biological,Tumor Markers, Biological: chemistry},
month = {apr},
number = {8},
pages = {1050--6},
pmid = {20189938},
title = {{Post hoc power estimation in large-scale multiple testing problems.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20189938},
volume = {26},
year = {2010}
}
@article{Henson2002,
abstract = {Recent parallels between neurophysiological and neuroimaging findings suggest that repeated stimulus processing produces decreased responses in brain regions associated with that processing--a 'repetition suppression' effect. In the present study, volunteers performed two tasks on repeated presentation of famous and unfamiliar faces during functional magnetic resonance imaging (fMRI). In the implicit task, they made fame-judgements (regardless of repetition); in the explicit task, they made episodic recognition judgements (regardless of familiarity). Only in the implicit task was repetition suppression observed: for famous faces in a right lateral fusiform region, and for both famous and unfamiliar faces in a left inferior occipital region. Repetition suppression is therefore not an automatic consequence of repeated perceptual processing of stimuli.},
author = {Henson, R N a and Shallice, T and Gorno-Tempini, M L and Dolan, R J},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Henson et al. - 2002 - Face repetition effects in implicit and explicit memory tests as measured by fMRI.pdf:pdf},
issn = {1047-3211},
journal = {Cerebral cortex},
keywords = {Adult,Attention,Attention: physiology,Face,Female,Humans,Magnetic Resonance Imaging,Male,Pattern Recognition,Recognition (Psychology),Recognition (Psychology): physiology,Visual,Visual: physiology},
month = {feb},
number = {2},
pages = {178--86},
pmid = {11739265},
title = {{Face repetition effects in implicit and explicit memory tests as measured by fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11739265},
volume = {12},
year = {2002}
}
@article{Taylor2005,
abstract = {Multiple testing issues are important in gene expression studies, where typically thousands of genes are compared over two or more experimental conditions. The false discovery rate has become a popular measure in this setting. Here we discuss a complementary measure, the 'miss rate', and show how to estimate it in practice.},
author = {Taylor, Jonathan and Tibshirani, Robert and Efron, Bradley},
doi = {10.1093/biostatistics/kxh021},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Taylor, Tibshirani, Efron - 2005 - The 'miss rate' for the analysis of gene expression data.pdf:pdf},
issn = {1465-4644},
journal = {Biostatistics (Oxford, England)},
keywords = {Computer Simulation,Data Interpretation, Statistical,False Negative Reactions,False Positive Reactions,Gene Expression Regulation, Leukemic,Gene Expression Regulation, Leukemic: genetics,Humans,Leukemia, Myeloid, Acute,Leukemia, Myeloid, Acute: genetics,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Precursor Cell Lymphoblastic Leukemia-Lymphoma,Precursor Cell Lymphoblastic Leukemia-Lymphoma: ge},
month = {jan},
number = {1},
pages = {111--7},
pmid = {15618531},
title = {{The 'miss rate' for the analysis of gene expression data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15618531},
volume = {6},
year = {2005}
}
@article{Friguet2011,
author = {Friguet, Chlo{\'{e}} and Causeur, David},
doi = {10.1016/j.csda.2011.03.016},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friguet, Causeur - 2011 - Estimation of the proportion of true null hypotheses in high-dimensional data under dependence.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics {\&} Data Analysis},
month = {sep},
number = {9},
pages = {2665--2676},
publisher = {Elsevier B.V.},
title = {{Estimation of the proportion of true null hypotheses in high-dimensional data under dependence}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0167947311001071},
volume = {55},
year = {2011}
}
@article{Auer2011,
abstract = {Current thresholding strategies for the analysis of functional MRI (fMRI) datasets may suffer from specific limitations (e.g. with respect to the required smoothness) or lead to reduced performance for a low signal-to-noise ratio (SNR). Although a previously proposed two-threshold (TT) method offers a promising solution to these problems, the use of preset settings limits its performance. This work presents an optimised TT approach that estimates the required parameters in an iterative manner.},
author = {Auer, Tibor and Schweizer, Renate and Frahm, Jens},
doi = {10.1007/s00330-011-2184-5},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Auer, Schweizer, Frahm - 2011 - An iterative two-threshold analysis for single-subject functional MRI of the human brain.pdf:pdf},
issn = {1432-1084},
journal = {European radiology},
keywords = {auer,biomedizinische nmr forschungs gmbh,frahm,functional mri,iterative optimisation,j,r,roc analysis,schweizer,t,thresholding,two-threshold},
month = {nov},
number = {11},
pages = {2369--87},
pmid = {21710268},
title = {{An iterative two-threshold analysis for single-subject functional MRI of the human brain.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21710268},
volume = {21},
year = {2011}
}
@article{Mumford2008,
abstract = {When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80{\%} power is not as simple as finding the smallest sample size capable of achieving 80{\%} power, since smaller sample sizes require each subject to be scanned longer.},
author = {Mumford, Jeanette A and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2007.07.061},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Mumford, Nichols - 2008 - Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Algorithms,Auditory,Auditory: physiology,Brain Mapping,Brain Mapping: methods,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Evoked Potentials,Female,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models,Neurological,Reproducibility of Results,Sensitivity and Specificity,Speech Perception,Speech Perception: physiology,Statistics as Topic},
month = {jan},
number = {1},
pages = {261--8},
pmid = {17919925},
title = {{Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2423281{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {39},
year = {2008}
}
@article{Norris2006,
abstract = {Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly changing from non-changing genes, especially because statistical significance must be considered in the context of multiple hypothesis testing. Here, we present a balanced probability analysis (BPA), which provides the biologist with an approach to interpret results in the context of the total number of genes truly differentially expressed and false discovery and false negative rates for the list of genes reaching any significance threshold. In situations where the changes are of modest magnitude, sole consideration of the false discovery rate can result in poor power to detect genes truly differentially expressed. Concomitant analysis of the rate of truly differentially expressed genes not identified, i.e., the false negative rate, allows balancing of the two error rates and a more thorough insight into the data. To this end, we have developed a unique, model-based procedure for the estimation of false negative rates, which allows application of BPA to real data in which changes are modest.},
author = {Norris, Andrew W and Kahn, C Ronald},
doi = {10.1073/pnas.0510115103},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Norris, Kahn - 2006 - Analysis of gene expression in pathophysiological states balancing false discovery and false negative rates.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {False Negative Reactions,False Positive Reactions,Gene Expression Profiling,Gene Expression Profiling: statistics {\&} numerical,Gene Expression Regulation,Gene Expression Regulation: physiology,Humans,Models, Genetic,Obesity, Morbid,Obesity, Morbid: genetics,Obesity, Morbid: physiopathology,Precursor Cell Lymphoblastic Leukemia-Lymphoma,Precursor Cell Lymphoblastic Leukemia-Lymphoma: ge,Precursor Cell Lymphoblastic Leukemia-Lymphoma: ph,Probability},
month = {jan},
number = {3},
pages = {649--53},
pmid = {16407153},
title = {{Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1334678{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {103},
year = {2006}
}
@article{Storey2003,
abstract = {With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.},
author = {Storey, John D and Tibshirani, Robert},
doi = {10.1073/pnas.1530509100},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Storey, Tibshirani - 2003 - Statistical significance for genomewide studies.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Algorithms,Alternative Splicing,Animals,Binding Sites,Exons,Gene Expression Regulation,Genetic Linkage,Genetic Techniques,Genome,Humans,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Statistics as Topic,Transcription, Genetic},
month = {aug},
number = {16},
pages = {9440--5},
pmid = {12883005},
title = {{Statistical significance for genomewide studies.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=170937{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {100},
year = {2003}
}
@article{Bennett2009,
abstract = {An incredible amount of data is generated in the course of a functional neuroimaging experiment. The quantity of data gives us improved temporal and spatial resolution with which to evaluate our results. It also creates a staggering multiple testing problem. A number of methods have been created that address the multiple testing problem in neuroimaging in a principled fashion. These methods place limits on either the familywise error rate (FWER) or the false discovery rate (FDR) of the results. These principled approaches are well established in the literature and are known to properly limit the amount of false positives across the whole brain. However, a minority of papers are still published every month using methods that are improperly corrected for the number of tests conducted. These latter methods place limits on the voxelwise probability of a false positive and yield no information on the global rate of false positives in the results. In this commentary, we argue in favor of a principled approach to the multiple testing problem--one that places appropriate limits on the rate of false positives across the whole brain gives readers the information they need to properly evaluate the results.},
author = {Bennett, Craig M and Wolford, George L and Miller, Michael B},
doi = {10.1093/scan/nsp053},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bennett, Wolford, Miller - 2009 - The principled control of false positives in neuroimaging.pdf:pdf},
issn = {1749-5024},
journal = {Social cognitive and affective neuroscience},
keywords = {Brain,Brain Mapping,Brain: blood supply,Brain: physiology,Data Interpretation, Statistical,False Positive Reactions,Humans,Magnetic Resonance Imaging},
month = {dec},
number = {4},
pages = {417--22},
pmid = {20042432},
title = {{The principled control of false positives in neuroimaging.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2799957{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {4},
year = {2009}
}
@article{Sarkar2012,
author = {Sarkar, Sanat K. and Guo, Wenge and Finner, Helmut},
doi = {10.1016/j.jspi.2011.06.022},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Sarkar, Guo, Finner - 2012 - On adaptive procedures controlling the familywise error rate.pdf:pdf},
issn = {03783758},
journal = {Journal of Statistical Planning and Inference},
keywords = {Adaptive methods,Familywise error rate,Multiple testing,Stepdown method,Stepup method},
month = {jan},
number = {1},
pages = {65--78},
publisher = {Elsevier},
title = {{On adaptive procedures controlling the familywise error rate}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0378375811002655},
volume = {142},
year = {2012}
}
@article{Lieberman2009,
abstract = {Statistical thresholding (i.e. P-values) in fMRI research has become increasingly conservative over the past decade in an attempt to diminish Type I errors (i.e. false alarms) to a level traditionally allowed in behavioral science research. In this article, we examine the unintended negative consequences of this single-minded devotion to Type I errors: increased Type II errors (i.e. missing true effects), a bias toward studying large rather than small effects, a bias toward observing sensory and motor processes rather than complex cognitive and affective processes and deficient meta-analyses. Power analyses indicate that the reductions in acceptable P-values over time are producing dramatic increases in the Type II error rate. Moreover, the push for a mapwide false discovery rate (FDR) of 0.05 is based on the assumption that this is the FDR in most behavioral research; however, this is an inaccurate assessment of the conventions in actual behavioral research. We report simulations demonstrating that combined intensity and cluster size thresholds such as P {\textless} 0.005 with a 10 voxel extent produce a desirable balance between Types I and II error rates. This joint threshold produces high but acceptable Type II error rates and produces a FDR that is comparable to the effective FDR in typical behavioral science articles (while a 20 voxel extent threshold produces an actual FDR of 0.05 with relatively common imaging parameters). We recommend a greater focus on replication and meta-analysis rather than emphasizing single studies as the unit of analysis for establishing scientific truth. From this perspective, Type I errors are self-erasing because they will not replicate, thus allowing for more lenient thresholding to avoid Type II errors.},
author = {Lieberman, Matthew D and Cunningham, William a},
doi = {10.1093/scan/nsp052},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Lieberman, Cunningham - 2009 - Type I and Type II error concerns in fMRI research re-balancing the scale.pdf:pdf},
issn = {1749-5024},
journal = {Social cognitive and affective neuroscience},
keywords = {Brain,Brain Mapping,Brain: blood supply,Data Interpretation, Statistical,False Positive Reactions,Humans,Image Interpretation, Computer-Assisted,Magnetic Resonance Imaging,Meta-Analysis as Topic,Oxygen,Oxygen: blood,Reproducibility of Results,Research Design,Sample Size},
month = {dec},
number = {4},
pages = {423--8},
pmid = {20035017},
title = {{Type I and Type II error concerns in fMRI research: re-balancing the scale.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2799956{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {4},
year = {2009}
}
@article{Smith2009,
abstract = {Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z {\textless} 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.},
author = {Smith, Stephen M and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2008.03.061},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Smith, Nichols - 2009 - Threshold-free cluster enhancement addressing problems of smoothing, threshold dependence and localisation in cl.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Area Under Curve,Brain Mapping,Brain Mapping: methods,Computer-Assisted,Computer-Assisted: methods,Humans,Image Enhancement,Image Enhancement: methods,Image Processing,ROC Curve},
month = {jan},
number = {1},
pages = {83--98},
pmid = {18501637},
title = {{Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18501637},
volume = {44},
year = {2009}
}
@article{Nichols2003,
abstract = {Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg's methods, are now applicable to dependent data. Continuous random field methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We find that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom. We also show the limitations of trying to find an equivalent number of independent tests for an image of correlated test statistics.},
author = {Nichols, Thomas E and Hayasaka, Satoru},
doi = {10.1191/0962280203sm341ra},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nichols, Hayasaka - 2003 - Controlling the familywise error rate in functional neuroimaging a comparative review.pdf:pdf},
institution = {Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. nichols@umich.edu},
issn = {09622802},
journal = {Statistical Methods in Medical Research},
month = {oct},
number = {5},
pages = {419--446},
publisher = {Sage},
title = {{Controlling the familywise error rate in functional neuroimaging: a comparative review}},
url = {http://dx.doi.org/10.1191/0962280203sm341ra http://smm.sagepub.com/cgi/doi/10.1191/0962280203sm341ra},
volume = {12},
year = {2003}
}
@article{Poldrack2008,
abstract = {In this editorial, we outline a set of guidelines for the reporting of methods and results in functional magnetic resonance imaging studies and provide a checklist to assist authors in preparing manuscripts that meet these guidelines.},
author = {Poldrack, Russell A and Fletcher, Paul C and Henson, Richard N and Worsley, Keith J and Brett, Matthew and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2007.11.048},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Poldrack et al. - 2008 - Guidelines for reporting an fMRI study.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Guidelines as Topic,Magnetic Resonance Imaging,Publishing,Publishing: standards,fMRI},
mendeley-tags = {fMRI},
month = {apr},
number = {2},
pages = {409--14},
pmid = {18191585},
title = {{Guidelines for reporting an fMRI study.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2287206{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {40},
year = {2008}
}
@article{Skudlarski1999,
abstract = {The complicated structure of fMRI signals and associated noise sources make it difficult to assess the validity of various steps involved in the statistical analysis of brain activation. Most methods used for fMRI analysis assume that observations are independent and that the noise can be treated as white gaussian noise. These assumptions are usually not true but it is difficult to assess how severely these assumptions are violated and what are their practical consequences. In this study a direct comparison is made between the power of various analytical methods used to detect activations, without reference to estimates of statistical significance. The statistics used in fMRI are treated as metrics designed to detect activations and are not interpreted probabilistically. The receiver operator characteristic (ROC) method is used to compare the efficacy of various steps in calculating an activation map in the study of a single subject based on optimizing the ratio of the number of detected activations to the number of false-positive findings. The main findings are as follows: Preprocessing. The removal of intensity drifts and high-pass filtering applied on the voxel time-course level is beneficial to the efficacy of analysis. Temporal normalization of the global image intensity, smoothing in the temporal domain, and low-pass filtering do not improve power of analysis. Choices of statistics. the cross-correlation coefficient and t-statistic, as well as nonparametric Mann-Whitney statistics, prove to be the most effective and are similar in performance, by our criterion. Task design. the proper design of task protocols is shown to be crucial. In an alternating block design the optimal block length is be approximately 18 s. Spatial clustering. an initial spatial smoothing of images is more efficient than cluster filtering of the statistical parametric activation maps.},
author = {Skudlarski, P and Constable, R T and Gore, J C},
doi = {10.1006/nimg.1999.0402},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Skudlarski, Constable, Gore - 1999 - ROC analysis of statistical methods used in functional MRI individual subjects.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Brain,Brain Mapping,Brain: anatomy {\&} histology,Brain: physiology,Computer Simulation,Fourier Analysis,Humans,MTP,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,ROC,ROC Curve,Statistics as Topic,Time Factors,fMRI},
mendeley-tags = {MTP,ROC,fMRI},
month = {mar},
number = {3},
pages = {311--29},
pmid = {10075901},
title = {{ROC analysis of statistical methods used in functional MRI: individual subjects.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10075901},
volume = {9},
year = {1999}
}
@article{Dudoit2003,
author = {Dudoit, Sandrine and Shaffer, Juliet Popper and Block, Jennifer C.},
doi = {10.1214/ss/1056397487},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Dudoit, Shaffer, Block - 2003 - Multiple Hypothesis Testing in Microarray Experiments.pdf:pdf},
issn = {0883-4237},
journal = {Statistical Science},
keywords = {MTP,adjusted p-value,and phrases,dna,false discovery rate,family-wise type i error,multiple hypothesis testing,permutation,rate},
mendeley-tags = {MTP},
month = {feb},
number = {1},
pages = {71--103},
title = {{Multiple Hypothesis Testing in Microarray Experiments}},
url = {http://projecteuclid.org/Dienst/getRecord?id=euclid.ss/1056397487/},
volume = {18},
year = {2003}
}
@article{Logan2004,
abstract = {This paper reviews and compares individual voxel-wise thresholding methods for identifying active voxels in single-subject fMRI datasets. Different error rates are described which may be used to calibrate activation thresholds. We discuss methods which control each of the error rates at a prespecified level alpha, including simple procedures which ignore spatial correlation among the test statistics as well as more elaborate ones which incorporate this correlation information. The operating characteristics of the methods are shown through a simulation study, indicating that the error rate used has an important impact on the sensitivity of the thresholding method, but that accounting for correlation has little impact. Therefore, the simple procedures described work well for thresholding most single-subject fMRI experiments and are recommended. The methods are illustrated with a real bilateral finger tapping experiment.},
author = {Logan, Brent R and Rowe, Daniel B},
doi = {10.1016/j.neuroimage.2003.12.047},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Logan, Rowe - 2004 - An evaluation of thresholding techniques in fMRI analysis.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Algorithms,Bayes Theorem,Computer Simulation,Computer-Assisted,Computer-Assisted: statistics {\&} numerical data,Fingers,Fingers: physiology,Humans,Image Processing,MTP,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Movement,Movement: physiology,evaluation,fMRI},
mendeley-tags = {MTP,evaluation,fMRI},
month = {may},
number = {1},
pages = {95--108},
pmid = {15110000},
title = {{An evaluation of thresholding techniques in fMRI analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15110000},
volume = {22},
year = {2004}
}
@article{Logan2008,
abstract = {Many fMRI experiments have a common objective of identifying active voxels in a neuroimaging dataset. This is done in single subject experiments, for example, by performing individual voxel-wise tests of the null hypothesis that the observed time course is not significantly related to an assigned reference function. A voxel activation map is then constructed by applying a thresholding rule to the resulting statistics (e.g., t-statistics). Typically the task-related activation is expected to occur in clusters of voxels rather than in isolated single voxels. A variety of spatial thresholding techniques have been proposed to reflect this belief, including smoothing the raw t-statistics, cluster size inference, and spatial mixture modeling. We study two aspects of these spatial thresholding procedures applied to single subject fMRI analysis through simulation. First, we examine the performance of these procedures in terms of sensitivity to detect voxel activation, using receiver operating characteristic curves. Second, we consider the accuracy of these spatial thresholding procedures in estimation of the size of the activation region, both in terms of bias and variance. The findings indicate that smoothing has the highest sensitivity to modest magnitude signals, but tend to overestimate the size of the activation region. Spatial mixture models estimate the size of a spatially distributed activation region well, but may be less sensitive to modest magnitude signals, indicating that additional research into more sensitive spatial mixture models is needed. Finally, the methods are illustrated with a real bilateral finger-tapping fMRI experiment.},
author = {Logan, Brent R and Geliazkova, Maya P and Rowe, Daniel B},
doi = {10.1002/hbm.20471},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Logan, Geliazkova, Rowe - 2008 - An evaluation of spatial thresholding techniques in fMRI analysis(2).pdf:pdf},
issn = {1097-0193},
journal = {Human brain mapping},
keywords = {Algorithms,Bias (Epidemiology),Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Fingers,Fingers: physiology,Humans,Image Processing,MTP,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Movement,Movement: physiology,Psychomotor Performance,Psychomotor Performance: physiology,Signal Processing,Software,Statistical,evaluation,fMRI,topological inference},
mendeley-tags = {MTP,evaluation,fMRI,topological inference},
month = {dec},
number = {12},
pages = {1379--89},
pmid = {18064589},
title = {{An evaluation of spatial thresholding techniques in fMRI analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18064589},
volume = {29},
year = {2008}
}
@article{Marchini2004,
abstract = {Approaches for the analysis of statistical parametric maps (SPMs) can be crudely grouped into three main categories in which different philosophies are applied to delineate activated regions. These being type I error control thresholding, false discovery rate (FDR) control thresholding and posterior probability thresholding. To better understand the properties of these main approaches, we carried out a simulation study to compare the approaches as they would be used on real data sets. Using default settings, we find that posterior probability thresholding is the most powerful approach, and type I error control thresholding provides the lowest levels of type I error. False discovery rate control thresholding performs in between the other approaches for both these criteria, although for some parameter settings this approach can approximate the performance of posterior probability thresholding. Based on these results, we discuss the relative merits of the three approaches in an attempt to decide upon an optimal approach. We conclude that viewing the problem of delineating areas of activation as a classification problem provides a highly interpretable framework for comparing the methods. Within this framework, we highlight the role of the loss function, which explicitly penalizes the types of errors that may occur in a given analysis.},
author = {Marchini, Jonathan and Presanis, Anne},
doi = {10.1016/j.neuroimage.2004.03.030},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Marchini, Presanis - 2004 - Comparing methods of analyzing fMRI statistical parametric maps.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Bayes Theorem,Computer Simulation,Data Interpretation,Differential Threshold,Linear Models,MTP,Magnetic Resonance Imaging,Probability,Statistical,fMRI},
mendeley-tags = {MTP,fMRI},
month = {jul},
number = {3},
pages = {1203--13},
pmid = {15219592},
title = {{Comparing methods of analyzing fMRI statistical parametric maps.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15219592},
volume = {22},
year = {2004}
}
@article{Lindquist2009a,
author = {Lindquist, Martin A and Gelman, Andrew},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Lindquist, Gelman - 2009 - Correlations and Multiple Comparisons in Functional Imaging.pdf:pdf},
journal = {Perspectives on Psychological Science},
keywords = {MTP,fMRI},
mendeley-tags = {MTP,fMRI},
number = {3},
pages = {310--313},
title = {{Correlations and Multiple Comparisons in Functional Imaging}},
volume = {4},
year = {2009}
}
@article{Friman2005,
abstract = {The problem of selecting a threshold for the statistical parameter maps in functional MRI (fMRI) is a delicate issue. The use of advanced test statistics and/or the complex dependence structure of fMRI noise may preclude parametric statistical methods for finding appropriate thresholds. Non-parametric statistical methodology has been presented as a feasible alternative. In this paper, we discuss resampling methods for finding thresholds in single subject fMRI analysis. It is shown that the presence of a BOLD response in the time series biases the estimation of the temporal autocorrelation, which in turn leads to biased thresholds. Therefore, proposed resampling methods based on Fourier and wavelet transforms, which employ implicit and weak models of the temporal noise characteristic, may produce erroneous thresholds. In contrast, resampling based on a pre-whitening transform, which is driven by an explicit noise model, is robust to the presence of a BOLD response. The size of the bias is, however, largely dependent on the complexity of the experimental design. While blocked designs can induce large biases, event-related designs generate significantly smaller biases. Results supporting these claims are provided.},
author = {Friman, Ola and Westin, Carl-Fredrik},
doi = {10.1016/j.neuroimage.2004.11.046},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friman, Westin - 2005 - Resampling fMRI time series.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Artifacts,Bias (Epidemiology),Brain,Brain Mapping,Brain: blood supply,Computer-Assisted,Computer-Assisted: statistics {\&} numerical data,Data Collection,Data Collection: statistics {\&} numerical data,Fourier Analysis,Humans,Image Enhancement,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: instrumentation,Nonparametric,Oxygen,Oxygen: blood,Reproducibility of Results,Signal Processing,Statistics,Statistics as Topic,Time Factors,resampling,time series},
mendeley-tags = {resampling,time series},
month = {apr},
number = {3},
pages = {859--67},
pmid = {15808986},
title = {{Resampling fMRI time series.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15808986},
volume = {25},
year = {2005}
}
@article{Shi2008,
abstract = {Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.},
author = {Shi, Leming and Jones, Wendell D and Jensen, Roderick V and Harris, Stephen C and Perkins, Roger G and Goodsaid, Federico M and Guo, Lei and Croner, Lisa J and Boysen, Cecilie and Fang, Hong and Qian, Feng and Amur, Shashi and Bao, Wenjun and Barbacioru, Catalin C and Bertholet, Vincent and Cao, Xiaoxi Megan and Chu, Tzu-Ming and Collins, Patrick J and Fan, Xiao-Hui and Frueh, Felix W and Fuscoe, James C and Guo, Xu and Han, Jing and Herman, Damir and Hong, Huixiao and Kawasaki, Ernest S and Li, Quan-Zhen and Luo, Yuling and Ma, Yunqing and Mei, Nan and Peterson, Ron L and Puri, Raj K and Shippy, Richard and Su, Zhenqiang and Sun, Yongming Andrew and Sun, Hongmei and Thorn, Brett and Turpaz, Yaron and Wang, Charles and Wang, Sue Jane and Warrington, Janet a and Willey, James C and Wu, Jie and Xie, Qian and Zhang, Liang and Zhang, Lu and Zhong, Sheng and Wolfinger, Russell D and Tong, Weida},
doi = {10.1186/1471-2105-9-S9-S10},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Shi et al. - 2008 - The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarra.pdf:pdf},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Algorithms,Computer Simulation,Data Interpretation,Gene Expression Profiling,Gene Expression Profiling: methods,Genes,Genes: genetics,Genetic,MTP,Models,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Reproducibility of Results,Sensitivity and Specificity,Statistical,balance,power,stability},
mendeley-tags = {MTP,balance,power,stability},
month = {jan},
pages = {S10},
pmid = {18793455},
title = {{The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2537561{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {9 Suppl 9},
year = {2008}
}
@article{Cai2010,
author = {Cai, T. Tony and Jin, Jiashun},
doi = {10.1214/09-AOS696},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Cai, Jin - 2010 - Optimal rates of convergence for estimating the null density and proportion of nonnull effects in large-scale multiple.pdf:pdf},
issn = {0090-5364},
journal = {The Annals of Statistics},
keywords = {pi0},
month = {feb},
number = {1},
pages = {100--145},
title = {{Optimal rates of convergence for estimating the null density and proportion of nonnull effects in large-scale multiple testing}},
url = {http://projecteuclid.org/euclid.aos/1262271611},
volume = {38},
year = {2010}
}
@article{Ge2009,
abstract = {The large-scale multiple testing problems resulting from the measurement of thousands of genes in microarray experiments have received increasing interest during the past several years. This article describes some commonly used criteria for controlling false positive errors, including familywise error rates, false discovery rates and false discovery proportion rates. Various statistical methods controlling these error rates are described. The advantages and disadvantages of these methods are discussed. These methods are applied to gene expression data from two microarray studies and the properties of these multiple testing procedures are compared.},
author = {Ge, Yongchao and Sealfon, Stuart C and Speed, Terence P},
doi = {10.1177/0962280209351899},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Ge, Sealfon, Speed - 2009 - Multiple testing and its applications to microarrays.pdf:pdf},
issn = {1477-0334},
journal = {Statistical methods in medical research},
keywords = {Algorithms,Data Interpretation,False Positive Reactions,Gene Expression Profiling,Gene Expression Profiling: methods,MTP,Models,Oligonucleotide Array Sequence Analysis,Statistical},
mendeley-tags = {MTP},
month = {dec},
number = {6},
pages = {543--63},
pmid = {20048384},
title = {{Multiple testing and its applications to microarrays.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20048384},
volume = {18},
year = {2009}
}
@article{Stolovitzky2003,
author = {Stolovitzky, Gustavo},
doi = {10.1016/S0959-440X(03)00078-2},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Stolovitzky - 2003 - Gene selection in microarray data the elephant, the blind men and our algorithms.pdf:pdf},
issn = {0959440X},
journal = {Current Opinion in Structural Biology},
keywords = {MTP},
month = {jun},
number = {3},
pages = {370--376},
title = {{Gene selection in microarray data: the elephant, the blind men and our algorithms}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0959440X03000782},
volume = {13},
year = {2003}
}
@article{Genovese2002,
abstract = {Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.},
author = {Genovese, Christopher R and Lazar, Nicole a and Nichols, Thomas E},
doi = {10.1006/nimg.2001.1037},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Genovese, Lazar, Nichols - 2002 - Thresholding of statistical maps in functional neuroimaging using the false discovery rate.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Artifacts,Brain Mapping,Brain Mapping: methods,Cerebral Cortex,Cerebral Cortex: physiology,Computer Simulation,Computer-Assisted,FDR,Female,Humans,Image Processing,MTP,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Mathematical Computing,Motor Activity,Motor Activity: physiology,Reference Values,fmri},
mendeley-tags = {FDR,MTP,fmri},
month = {apr},
number = {4},
pages = {870--8},
pmid = {11906227},
title = {{Thresholding of statistical maps in functional neuroimaging using the false discovery rate.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11906227},
volume = {15},
year = {2002}
}
@article{Genovese2006,
author = {Genovese, C. R. and Roeder, K. and Wasserman, L.},
doi = {10.1093/biomet/93.3.509},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Genovese, Roeder, Wasserman - 2006 - False discovery control with p-value weighting.pdf:pdf},
issn = {0006-3444},
journal = {Biometrika},
keywords = {FDR},
month = {sep},
number = {3},
pages = {509--524},
title = {{False discovery control with p-value weighting}},
url = {http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/93.3.509},
volume = {93},
year = {2006}
}
@article{Benjamini1995,
author = {Benjamini, Yoav and Hochberg, Y.},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Benjamini, Hochberg - 1995 - Controlling the false discovery rate a practical and powerful approach to multiple testing.pdf:pdf},
journal = {Journal of the Royal Statistical Society. Series B, Statistical methodology},
keywords = {FDR,MTP,bonferroni-type procedures,comparison procedures,familywise error rate,multiple-,p-values},
mendeley-tags = {FDR,MTP},
number = {1},
pages = {289--300},
title = {{Controlling the false discovery rate: a practical and powerful approach to multiple testing}},
volume = {57},
year = {1995}
}
@article{Posch2009,
author = {Posch, Martin and Zehetmayer, Sonja and Bauer, Peter},
doi = {10.1198/jasa.2009.0137},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Posch, Zehetmayer, Bauer - 2009 - Hunting for Significance With the False Discovery Rate.pdf:pdf},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {FDR,balance,false negative rate,group sequential tests,microarray analysis,power},
mendeley-tags = {FDR,balance,power},
month = {jun},
number = {486},
pages = {832--840},
title = {{Hunting for Significance With the False Discovery Rate}},
url = {http://pubs.amstat.org/doi/abs/10.1198/jasa.2009.0137},
volume = {104},
year = {2009}
}
@article{Benjamini2000,
author = {Benjamini, Yoav and Hochberg, Y.},
doi = {10.3102/10769986025001060},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Benjamini, Hochberg - 2000 - On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics.pdf:pdf},
issn = {1076-9986},
journal = {Journal of Educational and Behavioral Statistics},
keywords = {FDR,adaptive FDR,pi0},
mendeley-tags = {FDR,adaptive FDR,pi0},
month = {jan},
number = {1},
pages = {60--83},
title = {{On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics}},
url = {http://jeb.sagepub.com/cgi/doi/10.3102/10769986025001060},
volume = {25},
year = {2000}
}
@article{Leek2008,
abstract = {We develop a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We derive a low-dimensional set of random vectors, called a dependence kernel, that fully captures the dependence structure in an observed high-dimensional dataset. This result shows a surprising reversal of the "curse of dimensionality" in the high-dimensional hypothesis testing setting. We show theoretically that conditioning on a dependence kernel is sufficient to render statistical tests independent regardless of the level of dependence in the observed data. This framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology.},
author = {Leek, Jeffrey T and Storey, John D},
doi = {10.1073/pnas.0808709105},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Leek, Storey - 2008 - A general framework for multiple testing dependence.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Algorithms,Computer Simulation,MTP,Models,Software,Statistical,Statistics as Topic,pi0},
mendeley-tags = {MTP,pi0},
month = {dec},
number = {48},
pages = {18718--23},
pmid = {19033188},
title = {{A general framework for multiple testing dependence.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2586646{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {105},
year = {2008}
}
@article{Weeda2009,
abstract = {An important issue in the analysis of fMRI is how to account for the spatial smoothness of activated regions. In this article a method is proposed to accomplish this by modeling activated regions with Gaussian shapes. Hypothesis tests on the location, spatial extent, and amplitude of these regions are performed instead of hypothesis tests of individual voxels. This increases power and eases interpretation. Simulation studies show robust hypothesis tests under misspecification of the shape model, and increased power over standard techniques especially at low signal-to-noise ratios. An application to real single-subject data also indicates that the method has increased power over standard methods.},
author = {Weeda, Wouter D and Waldorp, Lourens J and Christoffels, Ingrid and Huizenga, Hilde M},
doi = {10.1002/hbm.20697},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Weeda et al. - 2009 - Activated region fitting a robust high-power method for fMRI analysis using parameterized regions of activation.pdf:pdf},
issn = {1097-0193},
journal = {Human brain mapping},
keywords = {Algorithms,Brain,Brain Mapping,Brain: physiology,Computer Simulation,Computer-Assisted,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Neurological,Normal Distribution,ROI,Signal Processing,power},
mendeley-tags = {ROI,power},
month = {aug},
number = {8},
pages = {2595--605},
pmid = {19172652},
title = {{Activated region fitting: a robust high-power method for fMRI analysis using parameterized regions of activation.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19172652},
volume = {30},
year = {2009}
}
@article{Heller2006,
abstract = {We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation. The advantages of this analysis over previous ones are both conceptual and statistical. Recognizing that the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data. Testing the cluster units has a two-fold statistical advantage over testing each voxel separately: the signal to noise ratio within the unit tested is higher, and the number of hypotheses tests compared is smaller. We suggest controlling FDR on clusters, i.e., the proportion of clusters rejected erroneously out of all clusters rejected and explain the meaning of controlling this error rate. We introduce the powerful adaptive procedure to control the FDR on clusters. We apply our cluster-based analysis (CBA) to both an event-related and a block design fMRI vision experiment and demonstrate its increased power over voxel-by-voxel analysis in these examples as well as in simulations.},
author = {Heller, Ruth and Stanley, Damian and Yekutieli, Daniel and Rubin, Nava and Benjamini, Yoav},
doi = {10.1016/j.neuroimage.2006.04.233},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Heller et al. - 2006 - Cluster-based analysis of FMRI data(2).pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Cluster Analysis,Functional Laterality,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Neurological,Occipital Lobe,Occipital Lobe: anatomy {\&} histology},
month = {nov},
number = {2},
pages = {599--608},
pmid = {16952467},
title = {{Cluster-based analysis of FMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16952467},
volume = {33},
year = {2006}
}
@article{Bellec2009,
abstract = {Computer simulations have played a critical role in functional magnetic resonance imaging (fMRI) research, notably in the validation of new data analysis methods. Many approaches have been used to generate fMRI simulations, but there is currently no generic framework to assess how realistic each one of these approaches may be. In this article, a statistical technique called parametric bootstrap was used to generate a simulation database that mimicked the parameters found in a real database, which comprised 40 subjects and five tasks. The simulations were evaluated by comparing the distributions of a battery of statistical measures between the real and simulated databases. Two popular simulation models were evaluated for the first time by applying the bootstrap framework. The first model was an additive mixture of multiple components and the second one implemented a non-linear motion process. In both models, the simulated components included the following brain dynamics: a baseline, physiological noise, neural activation and random noise. These models were found to successfully reproduce the relative variance of the components and the temporal autocorrelation of the fMRI time series. By contrast, the level of spatial autocorrelation was found to be drastically low using the additive model. Interestingly, the motion process in the second model intrisically generated some slow time drifts and increased the level of spatial autocorrelations. These experiments demonstrated that the bootstrap framework is a powerful new tool that can pinpoint the respective strengths and limitations of simulation models.},
author = {Bellec, Pierre and Perlbarg, Vincent and Evans, Alan C},
doi = {10.1016/j.mri.2009.05.034},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bellec, Perlbarg, Evans - 2009 - Bootstrap generation and evaluation of an fMRI simulation database.pdf:pdf},
issn = {1873-5894},
journal = {Magnetic resonance imaging},
keywords = {Adult,Brain Mapping,Brain Mapping: methods,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Databases,Factual,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Monte Carlo Method,Normal Distribution,Statistical,Theoretical,Time Factors,evaluation,resampling,simulations},
mendeley-tags = {evaluation,resampling,simulations},
month = {dec},
number = {10},
pages = {1382--96},
pmid = {19570641},
title = {{Bootstrap generation and evaluation of an fMRI simulation database.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2783846{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {27},
year = {2009}
}
@article{Bowman2005,
abstract = {Functional neuroimaging, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), plays an important role in identifying specific brain regions associated with experimental stimuli or psychiatric disorders such as schizophrenia. PET and fMRI produce massive data sets that contain both temporal correlations from repeated scans and complex spatial correlations. Several methods exist for handling temporal correlations, some of which rely on transforming the response data to induce either a known or an independence covariance structure. Despite the presence of spatial correlations between the volume elements (voxels) comprising a brain scan, conventional methods perform voxel-by-voxel analyses of measured brain activity. We propose a two-stage spatio-temporal model for the estimation and testing of localized activity. Our second-stage model specifies a spatial auto-regression, capturing correlations within neural processing clusters defined by a data-driven cluster analysis. We use maximum likelihood methods to estimate parameters from our spatial autoregressive model. Our model protects against type-I errors, enables the detection of both localized and regional activations (including volume of interest effects), provides information on functional connectivity in the brain, and establishes a framework to produce spatially smoothed maps of distributed brain activity for each individual. We illustrate the application of our model using PET data from a study of working memory in individuals with schizophrenia.},
author = {Bowman, F Dubois},
doi = {10.1093/biostatistics/kxi027},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bowman - 2005 - Spatio-temporal modeling of localized brain activity.pdf:pdf},
issn = {1465-4644},
journal = {Biostatistics (Oxford, England)},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Cluster Analysis,Memory,Memory: physiology,Models, Neurological,Positron-Emission Tomography,Schizophrenia,Schizophrenia: pathology},
month = {oct},
number = {4},
pages = {558--75},
pmid = {15843592},
title = {{Spatio-temporal modeling of localized brain activity.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15843592},
volume = {6},
year = {2005}
}
@article{Hayasaka2004,
abstract = {Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decreased sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K.J., 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779-780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [NeuroImage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RFT, and nonstationary RFT methods. Using simulated data, we found that our permutation test performs well under any setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RFT cluster-size test.},
author = {Hayasaka, Satoru and Phan, K Luan and Liberzon, Israel and Worsley, Keith J and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2004.01.041},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hayasaka et al. - 2004 - Nonstationary cluster-size inference with random field and permutation methods.pdf:pdf},
isbn = {1734763221},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Computer-Assisted,Computer-Assisted: methods,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Neurological,Observer Variation,RFT,Statistical,resampling,topological inference},
mendeley-tags = {RFT,resampling,topological inference},
month = {jun},
number = {2},
pages = {676--87},
pmid = {15193596},
title = {{Nonstationary cluster-size inference with random field and permutation methods.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15193596},
volume = {22},
year = {2004}
}
@article{Pantazis2005,
abstract = {We describe the use of random field and permutation methods to detect activation in cortically constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps event-related MEG to a coregistered cortical surface. These approaches also extend directly to images computed from event-related EEG data. We determine statistical thresholds that control the familywise error rate (FWER) across space or across both space and time. Both random field and permutation methods use the distribution of the maximum statistic under the null hypothesis to find FWER thresholds. The former methods make assumptions on the distribution and smoothness of the data and use approximate analytical solutions, the latter resample the data and rely on empirical distributions. Both methods account for spatial and temporal correlation in the cortical maps. Unlike previous nonparametric work in neuroimaging, we address the problem of nonuniform specificity that can arise without a Gaussianity assumption. We compare and evaluate the methods on simulated data and experimental data from a somatosensory-evoked response study. We find that the random field methods are conservative with or without smoothing, though with a 5 vertex FWHM smoothness, they are close to exact. Our permutation methods demonstrated exact specificity in simulation studies. In real data, the permutation method was not as sensitive as the RF method, although this could be due to violations of the random field theory assumptions.},
author = {Pantazis, Dimitrios and Nichols, Thomas E and Baillet, Sylvain and Leahy, Richard M},
doi = {10.1016/j.neuroimage.2004.09.040},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Pantazis et al. - 2005 - A comparison of random field theory and permutation methods for the statistical analysis of MEG data.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Data Interpretation,Humans,Magnetoencephalography,Magnetoencephalography: statistics {\&} numerical dat,RFT,Sensitivity and Specificity,Statistical,resampling},
mendeley-tags = {RFT,resampling},
month = {apr},
number = {2},
pages = {383--94},
pmid = {15784416},
title = {{A comparison of random field theory and permutation methods for the statistical analysis of MEG data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15784416},
volume = {25},
year = {2005}
}
@article{Zhang2009,
abstract = {Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.},
author = {Zhang, Hui and Nichols, Thomas E and Johnson, Timothy D},
doi = {10.1016/j.neuroimage.2008.08.017},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zhang, Nichols, Johnson - 2009 - Cluster mass inference via random field theory.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: radionuclide imaging,Computer-Assisted,Computer-Assisted: methods,Humans,Image Processing,MTP,Magnetic Resonance Imaging,RFT,fMRI,topological inference},
mendeley-tags = {MTP,RFT,fMRI,topological inference},
month = {jan},
number = {1},
pages = {51--61},
pmid = {18805493},
publisher = {Elsevier Inc.},
title = {{Cluster mass inference via random field theory.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2739659{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {44},
year = {2009}
}
@article{Hartvig2000,
abstract = {Recently, Everitt and Bullmore [1999] proposed a mixture model for a test statistic for activation in fMRI data. The distribution of the statistic was divided into two components; one for nonactivated voxels and one for activated voxels. In this framework one can calculate a posterior probability for a voxel being activated, which provides a more natural basis for thresholding the statistic image, than that based on P-values. In this article, we extend the method of Everitt and Bullmore to account for spatial coherency of activated regions. We achieve this by formulating a model for the activation in a small region of voxels and using this spatial structure when calculating the posterior probability of a voxel being activated. We have investigated several choices of spatial models but find that they all work equally well for brain imaging data. We applied the model to synthetic data from statistical image analysis, a synthetic fMRI data set and to visual stimulation data. Our conclusion is that the method improves the estimation of the activation pattern significantly, compared to the nonspatial model and to smoothing the data with a kernel of FWHM 3 voxels. The difference between FWHM 2 smoothing and our method were more modest.},
author = {Hartvig, N V and Jensen, J L},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hartvig, Jensen - 2000 - Spatial mixture modeling of fMRI data.pdf:pdf},
issn = {1065-9471},
journal = {Human brain mapping},
keywords = {Brain,Brain: physiology,Computer Simulation,Humans,Magnetic Resonance Imaging,Models,Neurological,Photic Stimulation,bayes},
mendeley-tags = {bayes},
month = {dec},
number = {4},
pages = {233--48},
pmid = {11144753},
title = {{Spatial mixture modeling of fMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11144753},
volume = {11},
year = {2000}
}
@article{Schwartzman2009a,
abstract = {Current strategies for thresholding statistical parametric maps in neuroimaging include control of the family-wise error rate, control of the false discovery rate (FDR) and thresholding of the posterior probability of a voxel being active given the data, the latter derived from a mixture model of active and inactive voxels. Correct inference using any of these criteria depends crucially on the specification of the null distribution of the test statistics. In this article we show examples from fMRI and DTI data where the theoretical null distribution does not match well the observed distribution of the test statistics. As a solution, we introduce the use of an empirical null, a null distribution empirically estimated from the data itself, allowing for global corrections of theoretical null assumptions. The theoretical null distributions considered are normal, t, chi(2) and F, all commonly encountered in neuroimaging. The empirical null estimate is accompanied by an estimate of the proportion of non-active voxels in the data. Based on the two-class mixture model, we present the equivalence between the strategies of controlling FDR and thresholding posterior probabilities in the context of neuroimaging and show that the FDR estimates derived from the empirical null can be seen as empirical Bayes estimates.},
author = {Schwartzman, Armin and Dougherty, Robert F and Lee, Jongho and Ghahremani, Dara and Taylor, Jonathan E},
doi = {10.1016/j.neuroimage.2008.04.182},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Schwartzman et al. - 2009 - Empirical null and false discovery rate analysis in neuroimaging.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Artifacts,Brain,Brain Mapping,Brain Mapping: methods,Brain: radionuclide imaging,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Imaging, Three-Dimensional},
month = {jan},
number = {1},
pages = {71--82},
pmid = {18547821},
title = {{Empirical null and false discovery rate analysis in neuroimaging.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18547821},
volume = {44},
year = {2009}
}
@article{Nandy2007,
abstract = {One of the most important considerations in any hypothesis based fMRI data analysis is to choose the appropriate threshold to construct the activation maps, which is usually based on p-values. However, in fMRI data, there are three factors which necessitate severe corrections in the process of estimating the p-values. First, the fMRI time series at an individual voxel has strong temporal autocorrelation which needs to be estimated to obtain the corrected parametric p-value. The second factor is the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation. A common way in the statistical literature to account for multiple testing is to consider the family-wise error rate (FWE) which is related to the distribution of the maximum observed value over all voxels. The third problem, which is not mentioned frequently in the context of adjusting the p-value, is the effect of inherent low frequency processes present even in resting-state data that may introduce a large number of false positives without proper adjustment. In this article, a novel and efficient semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems mentioned above. The new method makes very few assumptions and demands minimal computational effort, unlike other existing resampling methods in fMRI. Furthermore, it will be demonstrated that the correction for temporal autocorrelation is not critical in implementing the proposed method. Results using the proposed method are compared with SPM2.},
author = {Nandy, Rajesh and Cordes, Dietmar},
doi = {10.1016/j.neuroimage.2006.10.025},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nandy, Cordes - 2007 - A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Brain,Brain: anatomy {\&} histology,Brain: physiology,Evoked Potentials,FWER,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Neurological,Reproducibility of Results,Sensitivity and Specificity,Statistical,bayes,resting-state},
mendeley-tags = {FWER,bayes,resting-state},
month = {feb},
number = {4},
pages = {1562--76},
pmid = {17196400},
title = {{A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17196400},
volume = {34},
year = {2007}
}
@article{Boulesteix2009,
abstract = {Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector.},
author = {Boulesteix, Anne-Laure and Slawski, Martin},
doi = {10.1093/bib/bbp034},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Boulesteix, Slawski - 2009 - Stability and aggregation of ranked gene lists.pdf:pdf},
issn = {1477-4054},
journal = {Briefings in bioinformatics},
keywords = {Algorithms,Databases,Gene Expression Profiling,Gene Expression Profiling: methods,Genetic,Models,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Software,Statistical,stability},
mendeley-tags = {stability},
month = {sep},
number = {5},
pages = {556--68},
pmid = {19679825},
title = {{Stability and aggregation of ranked gene lists.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19679825},
volume = {10},
year = {2009}
}
@article{Efron2012,
author = {Efron, Bradley and Tibshirani, Robert and Storey, John D and Tusher, Virginia},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron et al. - 2012 - Empirical Bayes Analysis of Microarray Experiment.pdf:pdf},
journal = {Journal of the American Statistical Association},
number = {456},
pages = {1151--1160},
title = {{Empirical Bayes Analysis of Microarray Experiment}},
volume = {96},
year = {2012}
}
@article{Bellec2010,
abstract = {A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.},
author = {Bellec, Pierre and Rosa-Neto, Pedro and Lyttelton, Oliver C and Benali, Habib and Evans, Alan C},
doi = {10.1016/j.neuroimage.2010.02.082},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bellec et al. - 2010 - Multi-level bootstrap analysis of stable clusters in resting-state fMRI.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Adult,Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Cluster Analysis,Computer-Assisted,Computer-Assisted: methods,Evoked Potentials,Evoked Potentials: physiology,Female,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Nerve Net,Nerve Net: physiology,Reproducibility of Results,Rest,Rest: physiology,Sensitivity and Specificity,Young Adult,resampling,resting-state,stability},
mendeley-tags = {resampling,resting-state,stability},
month = {jul},
number = {3},
pages = {1126--39},
pmid = {20226257},
publisher = {Elsevier Inc.},
title = {{Multi-level bootstrap analysis of stable clusters in resting-state fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20226257},
volume = {51},
year = {2010}
}
@article{Pavlidis2003,
author = {Pavlidis, P. and Li, Q. and Noble, W. S.},
doi = {10.1093/bioinformatics/btg227},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Pavlidis, Li, Noble - 2003 - The effect of replication on gene expression microarray experiments.pdf:pdf},
issn = {1367-4803},
journal = {Bioinformatics},
keywords = {stability},
mendeley-tags = {stability},
month = {sep},
number = {13},
pages = {1620--1627},
title = {{The effect of replication on gene expression microarray experiments}},
url = {http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btg227},
volume = {19},
year = {2003}
}
@article{Thirion2007,
abstract = {Group studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses.},
author = {Thirion, Bertrand and Pinel, Philippe and Tucholka, Alan and Roche, Alexis and Ciuciu, Philippe and Mangin, Jean-Fran{\c{c}}ois and Poline, Jean-Baptiste},
doi = {10.1109/TMI.2007.903226},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Thirion et al. - 2007 - Structural analysis of fMRI data revisited improving the sensitivity and reliability of fMRI group studies.pdf:pdf},
issn = {0278-0062},
journal = {IEEE transactions on medical imaging},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Computer-Assisted,Computer-Assisted: methods,Evoked Potentials,Evoked Potentials: physiology,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Information Storage and Retrieval,Information Storage and Retrieval: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Subtraction Technique,balance,power,stability},
mendeley-tags = {balance,power,stability},
month = {sep},
number = {9},
pages = {1256--69},
pmid = {17896597},
title = {{Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17896597},
volume = {26},
year = {2007}
}
@article{Zhang2008,
abstract = {MOTIVATION: Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries. RESULTS: Based on a statistical model, we show that even in technical replicate tests using identical samples, it is highly likely that the selected DEG lists will be very inconsistent in the presence of small measurement variations. Therefore, the apparently low reproducibility of DEG detection from current technical replicate tests does not indicate low quality of microarray technology. We also demonstrate that heterogeneous biological variations existing in real cancer data will further reduce the overall reproducibility of DEG detection. Nevertheless, in small subsamples from both simulated and real data, the actual false discovery rate (FDR) for each DEG list tends to be low, suggesting that each separately determined list may comprise mostly true DEGs. Rather than simply counting the overlaps of the discovery lists from different studies for a complex disease, novel metrics are needed for evaluating the reproducibility of discoveries characterized with correlated molecular changes. Supplementaty information: Supplementary data are available at Bioinformatics online.},
author = {Zhang, Min and Yao, Chen and Guo, Zheng and Zou, Jinfeng and Zhang, Lin and Xiao, Hui and Wang, Dong and Yang, Da and Gong, Xue and Zhu, Jing and Li, Yanhui and Li, Xia},
doi = {10.1093/bioinformatics/btn365},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2008 - Apparently low reproducibility of true differential expression discoveries in microarray studies.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Gene Expression Profiling,Models,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Reproducibility of Results,Statistical,stability},
mendeley-tags = {stability},
month = {sep},
number = {18},
pages = {2057--63},
pmid = {18632747},
title = {{Apparently low reproducibility of true differential expression discoveries in microarray studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18632747},
volume = {24},
year = {2008}
}
@article{Stiglic2010,
abstract = {This paper presents an empirical study that aims to explain the relationship between the number of samples and stability of different gene selection techniques for microarray datasets. Unlike other similar studies where number of genes in a ranked gene list is variable, this study uses an alternative approach where stability is observed at different number of samples that are used for gene selection. Three different metrics of stability, including a novel metric in bioinformatics, were used to estimate the stability of the ranked gene lists. Results of this study demonstrate that the univariate selection methods produce significantly more stable ranked gene lists than the multivariate selection methods used in this study. More specifically, thousands of samples are needed for these multivariate selection methods to achieve the same level of stability any given univariate selection method can achieve with only hundreds.},
author = {Stiglic, Gregor and Kokol, Peter},
doi = {10.1155/2010/616358},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Stiglic, Kokol - 2010 - Stability of ranked gene lists in large microarray analysis studies.pdf:pdf},
issn = {1110-7251},
journal = {Journal of biomedicine {\&} biotechnology},
keywords = {Area Under Curve,Breast Neoplasms,Breast Neoplasms: genetics,Colonic Neoplasms,Colonic Neoplasms: genetics,Databases,Female,Gene Expression Regulation,Genes,Genetic,Humans,Neoplasm,Neoplasm: genetics,Neoplastic,Oligonucleotide Array Sequence Analysis,stability},
mendeley-tags = {stability},
month = {jan},
pages = {616358},
pmid = {20625502},
title = {{Stability of ranked gene lists in large microarray analysis studies.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2896709{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {2010},
year = {2010}
}
@article{Woolrich2001,
abstract = {In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or "prewhitening," gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currently not available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 x 10(-5).},
author = {Woolrich, M W and Ripley, B D and Brady, M and Smith, Stephen M},
doi = {10.1006/nimg.2001.0931},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Woolrich et al. - 2001 - Temporal autocorrelation in univariate linear modeling of FMRI data.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Artifacts,Brain,Brain: anatomy {\&} histology,Brain: physiology,Calibration,Computer-Assisted,Fourier Analysis,Humans,Image Enhancement,Image Processing,Linear Models,Magnetic Resonance Imaging,temporal correlation},
mendeley-tags = {temporal correlation},
month = {dec},
number = {6},
pages = {1370--86},
pmid = {11707093},
title = {{Temporal autocorrelation in univariate linear modeling of FMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11707093},
volume = {14},
year = {2001}
}
@article{Hayasaka2007,
abstract = {Determining power and sample size in neuroimaging studies is a challenging task because of the massive multiple comparisons among tens of thousands of correlated voxels. To facilitate this task, we propose a power analysis method based on random field theory (RFT) by modeling signal areas within images as non-central random field. With this framework, power can be calculated for specific areas of anticipated signals within the brain while accounting for the 3D nature of signals. This framework can also be extended to visualize local variability in sensitivity as a power map and a sample size map. We validated our non-central RFT framework based on Monte-Carlo simulations. Moreover, we applied our method to a blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) data set with a small sample size in order to demonstrate its use in study planning. From the simulations, we found that our method was able to estimate power quite accurately. In the fMRI data analysis, despite the small sample size, we were able to determine power and the number of subjects required to detect signals.},
author = {Hayasaka, Satoru and Peiffer, Ann M and Hugenschmidt, Christina E and Laurienti, Paul J},
doi = {10.1016/j.neuroimage.2007.06.009},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hayasaka et al. - 2007 - Power and sample size calculation for neuroimaging studies by non-central random field theory.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adolescent,Adult,Auditory,Auditory Cortex,Auditory Cortex: physiology,Auditory Perception,Auditory Perception: physiology,Auditory: physiology,Brain Mapping,Brain Mapping: methods,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Data Interpretation,Echo-Planar Imaging,Echo-Planar Imaging: methods,Evoked Potentials,Female,Humans,Image Interpretation,Male,Middle Aged,Models,Neurological,RFT,Sample Size,Statistical,power},
mendeley-tags = {RFT,power},
month = {sep},
number = {3},
pages = {721--30},
pmid = {17658273},
title = {{Power and sample size calculation for neuroimaging studies by non-central random field theory.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2041809{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {37},
year = {2007}
}
@article{Desmond2002,
abstract = {Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80{\%} power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power.},
author = {Desmond, John E and Glover, Gary H},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Desmond, Glover - 2002 - Estimating sample size in functional MRI (fMRI) neuroimaging studies statistical power analyses.pdf:pdf},
issn = {0165-0270},
journal = {Journal of neuroscience methods},
keywords = {Adult,Analysis of Variance,Brain,Brain: physiology,Data Interpretation,Evoked Potentials,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Male,Sample Size,Statistical,group studies,power},
mendeley-tags = {group studies,power},
month = {aug},
number = {2},
pages = {115--28},
pmid = {12204303},
title = {{Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12204303},
volume = {118},
year = {2002}
}
@article{Smith2007,
abstract = {Optimising the efficiency of an experimental design is known to be of great importance. However, existing methods for calculating design rank deficiency and contrast estimability (an important aspect of experimental design) relate to computational precision rather than image noise and are therefore not very meaningful. For example, a contrast between two experimental conditions may be mathematically "estimable" while requiring a huge differential BOLD response for statistical significance to be reached. In this paper we formulate standard efficiency equations in terms of required BOLD effect, and use this to generate measures of rank/estimability which are meaningful. This takes into account the strength and smoothness of the timeseries noise and is applicable to complex contrasts; we show how to re-express several regressors and an associated contrast vector as a single equivalent regressor, so that we can calculate the contrast's effective peak-peak height unambiguously. We also present some example results on typical designs, and characterise noise results from a range of typical FMRI acquisitions, in order to allow experimenters to apply efficiency estimation in advance of acquiring data.},
author = {Smith, Stephen M and Jenkinson, Mark and Beckmann, Christian F and Miller, Karla and Woolrich, Mark},
doi = {10.1016/j.neuroimage.2006.09.019},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Smith et al. - 2007 - Meaningful design and contrast estimability in FMRI.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Artifacts,CNR,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Magnetic Resonance Imaging: statistics {\&} numerical,Research Design,design},
mendeley-tags = {CNR,design},
month = {jan},
number = {1},
pages = {127--36},
pmid = {17070706},
title = {{Meaningful design and contrast estimability in FMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17070706},
volume = {34},
year = {2007}
}
@article{Viviani2010,
author = {Viviani, Roberto},
doi = {10.1016/j.neuroimage.2009.10.085.},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Viviani - 2010 - Unbiased ROI selection in neuroimaging studies of individual differences.pdf:pdf},
journal = {Changes},
keywords = {ROI},
mendeley-tags = {ROI},
pages = {184--189},
title = {{Unbiased ROI selection in neuroimaging studies of individual differences}},
year = {2010}
}
@article{Wager2009,
abstract = {Making sense of a neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.},
author = {Wager, Tor D and Lindquist, Martin a and Nichols, Thomas E and Kober, Hedy and {Van Snellenberg}, Jared X},
doi = {10.1016/j.neuroimage.2008.10.061},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wager et al. - 2009 - Evaluating the consistency and specificity of neuroimaging data using meta-analysis.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Humans,Magnetic Resonance Imaging,Meta-Analysis as Topic,Positron-Emission Tomography,Sensitivity and Specificity,meta-analysis,power,stability},
mendeley-tags = {meta-analysis,power,stability},
month = {mar},
number = {1 Suppl},
pages = {S210--21},
pmid = {19063980},
publisher = {Elsevier Inc.},
title = {{Evaluating the consistency and specificity of neuroimaging data using meta-analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19063980},
volume = {45},
year = {2009}
}
@article{Wager2007,
abstract = {Meta-analysis is an increasingly popular and valuable tool for summarizing results across many neuroimaging studies. It can be used to establish consensus on the locations of functional regions, test hypotheses developed from patient and animal studies and develop new hypotheses on structure-function correspondence. It is particularly valuable in neuroimaging because most studies do not adequately correct for multiple comparisons; based on statistical thresholds used, we estimate that roughly 10-20{\%} of reported activations in published studies are false positives. In this article, we briefly summarize some of the most popular meta-analytic approaches and their limitations, and we outline a revised multilevel approach with increased validity for establishing consistency across studies. We also discuss multivariate methods by which meta-analysis can be used to develop and test hypotheses about co-activity of brain regions. Finally, we argue that meta-analyses can make a uniquely valuable contribution to predicting psychological states from patterns of brain activity, and we briefly discuss some methods for making such predictions.},
author = {Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},
doi = {10.1093/scan/nsm015},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wager, Lindquist, Kaplan - 2007 - Meta-analysis of functional neuroimaging data current and future directions.pdf:pdf},
issn = {1749-5024},
journal = {Social cognitive and affective neuroscience},
keywords = {Forecasting,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: trends,meta-analysis},
mendeley-tags = {meta-analysis},
month = {jun},
number = {2},
pages = {150--8},
pmid = {18985131},
title = {{Meta-analysis of functional neuroimaging data: current and future directions.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2555451{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {2},
year = {2007}
}
@article{Turkeltaub2002,
author = {Turkeltaub, Peter E. and Eden, Guinevere F. and Jones, Karen M. and Zeffiro, Thomas a.},
doi = {10.1006/nimg.2002.1131},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Turkeltaub et al. - 2002 - Meta-Analysis of the Functional Neuroanatomy of Single-Word Reading Method and Validation.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
keywords = {meta-analysis},
month = {jul},
number = {3},
pages = {765--780},
title = {{Meta-Analysis of the Functional Neuroanatomy of Single-Word Reading: Method and Validation}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811902911316},
volume = {16},
year = {2002}
}
@article{Sergerie2008,
abstract = {Functional neuroimaging studies have provided strong support for a critical role of the amygdala in emotional processing. However, several controversies remain in terms of whether different factors-such as sex, valence and stimulus type-have an effect on the magnitude and lateralization of amygdala responses. To address these issues, we conducted a meta-analysis of functional neuroimaging studies of visual emotional perception that reported amygdala activation. Critically, unlike previous neuroimaging meta-analyses, we took into account the magnitude (effect size) and reliability (variance) associated with each of the activations. Our results confirm that the amygdala responds to both positive and negative stimuli, with a preference for faces depicting emotional expressions. We did not find evidence for amygdala lateralization as a function of sex or valence. Instead, our findings provide strong support for a functional dissociation between left and right amygdala in terms of temporal dynamics. Taken together, results from this meta-analysis shed new light on several of the models proposed in the literature regarding the neural basis of emotional processing.},
author = {Sergerie, Karine and Chochol, Caroline and Armony, Jorge L},
doi = {10.1016/j.neubiorev.2007.12.002},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Sergerie, Chochol, Armony - 2008 - The role of the amygdala in emotional processing a quantitative meta-analysis of functional neuroimag.pdf:pdf},
issn = {0149-7634},
journal = {Neuroscience and biobehavioral reviews},
keywords = {Amygdala,Amygdala: anatomy {\&} histology,Amygdala: physiology,Animals,Brain Mapping,Diagnostic Imaging,Emotions,Emotions: physiology,Humans,meta-analysis},
mendeley-tags = {meta-analysis},
month = {jan},
number = {4},
pages = {811--30},
pmid = {18316124},
title = {{The role of the amygdala in emotional processing: a quantitative meta-analysis of functional neuroimaging studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18316124},
volume = {32},
year = {2008}
}
@article{Fusar-Poli2009,
abstract = {Most of our social interactions involve perception of emotional information from the faces of other people. Furthermore, such emotional processes are thought to be aberrant in a range of clinical disorders, including psychosis and depression. However, the exact neurofunctional maps underlying emotional facial processing are not well defined.},
author = {Fusar-Poli, Paolo and Placentino, Anna and Carletti, Francesco and Landi, Paola and Allen, Paul and Surguladze, Simon and Benedetti, Francesco and Abbamonte, Marta and Gasparotti, Roberto and Barale, Francesco and Perez, Jorge and McGuire, Philip and Politi, Pierluigi},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Fusar-Poli et al. - 2009 - Functional atlas of emotional faces processing a voxel-based meta-analysis of 105 functional magnetic resonan.pdf:pdf},
issn = {1488-2434},
journal = {Journal of psychiatry {\&} neuroscience : JPN},
keywords = {Adolescent,Adult,Aging,Aging: physiology,Anger,Brain,Brain: anatomy {\&} histology,Brain: physiology,Emotions,Emotions: physiology,Face,Face: physiology,Facial Expression,Fear,Fear: psychology,Female,Humans,Magnetic Resonance Imaging,Male,Middle Aged,Nerve Net,Sex Characteristics,Young Adult,meta-analysis},
mendeley-tags = {meta-analysis},
month = {nov},
number = {6},
pages = {418--32},
pmid = {19949718},
title = {{Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2783433{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {34},
year = {2009}
}
@article{Richlan2010,
author = {Richlan, Fabio and Kronbichler, Martin and Wimmer, Heinz},
doi = {10.1002/hbm.20752.Functional},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Richlan, Kronbichler, Wimmer - 2010 - UKPMC Funders Group Functional abnormalities in the dyslexic brain A quantitative meta-analysis o.pdf:pdf},
journal = {Brain},
keywords = {cerebral cortex,dyslexia,magnetic resonance imaging,meta-analysis,positron-emission tomography,reading},
mendeley-tags = {meta-analysis},
number = {10},
pages = {3299--3308},
title = {{UKPMC Funders Group Functional abnormalities in the dyslexic brain : A quantitative meta-analysis of neuroimaging studies}},
volume = {30},
year = {2010}
}
@article{Christ2009,
abstract = {Previous neuroimaging studies have implicated the prefrontal cortex (PFC) and nearby brain regions in deception. This is consistent with the hypothesis that lying involves the executive control system. To date, the nature of the contribution of different aspects of executive control to deception, however, remains unclear. In the present study, we utilized an activation likelihood estimate (ALE) method of meta-analysis to quantitatively identify brain regions that are consistently more active for deceptive responses relative to truthful responses across past studies. We then contrasted the results with additional ALE maps generated for 3 different aspects of executive control: working memory, inhibitory control, and task switching. Deception-related regions in dorsolateral PFC and posterior parietal cortex were selectively associated with working memory. Additional deception regions in ventrolateral PFC, anterior insula, and anterior cingulate cortex were associated with multiple aspects of executive control. In contrast, deception-related regions in bilateral inferior parietal lobule were not associated with any of the 3 executive control constructs. Our findings support the notion that executive control processes, particularly working memory, and their associated neural substrates play an integral role in deception. This work provides a foundation for future research on the neurocognitive basis of deception.},
author = {Christ, Shawn E and {Van Essen}, David C and Watson, Jason M and Brubaker, Lindsay E and McDermott, Kathleen B},
doi = {10.1093/cercor/bhn189},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Christ et al. - 2009 - The contributions of prefrontal cortex and executive control to deception evidence from activation likelihood est.pdf:pdf},
issn = {1460-2199},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
keywords = {Brain Mapping,Brain Mapping: methods,Deception,Evoked Potentials,Evoked Potentials: physiology,Feedback,Feedback: physiology,Humans,Lie Detection,Likelihood Functions,Prefrontal Cortex,Prefrontal Cortex: physiology,meta-analysis},
mendeley-tags = {meta-analysis},
month = {jul},
number = {7},
pages = {1557--66},
pmid = {18980948},
title = {{The contributions of prefrontal cortex and executive control to deception: evidence from activation likelihood estimate meta-analyses.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2693617{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {19},
year = {2009}
}
@article{Kober2010,
author = {Kober, Hedy and Wager, Tor D.},
doi = {10.1002/wcs.41},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Kober, Wager - 2010 - Meta-analysis of neuroimaging data.pdf:pdf},
issn = {19395078},
journal = {Wiley Interdisciplinary Reviews: Cognitive Science},
keywords = {meta-analysis},
mendeley-tags = {meta-analysis},
number = {2},
pages = {293----300},
title = {{Meta-analysis of neuroimaging data}},
url = {http://doi.wiley.com/10.1002/wcs.41},
volume = {1},
year = {2010}
}
@article{Salimi-Khorshidi2009,
abstract = {With the rapid growth of neuroimaging research and accumulation of neuroinformatic databases the synthesis of consensus findings using meta-analysis is becoming increasingly important. Meta-analyses pool data across many studies to identify reliable experimental effects and characterize the degree of agreement across studies. Coordinate-based meta-analysis (CBMA) methods are the standard approach, where each study entered into the meta-analysis has been summarized using only the (x, y, z) locations of peak activations (with or without activation magnitude) reported in published reports. Image-based meta-analysis (IBMA) methods use the full statistic images, and allow the use of hierarchical mixed effects models that account for differing intra-study variance and modeling of random inter-study variation. The purpose of this work is to compare image-based and coordinate-based meta-analysis methods applied to the same dataset, a group of 15 fMRI studies of pain, and to quantify the information lost by working only with the coordinates of peak activations instead of the full statistic images. We apply a 3-level IBMA mixed model for a "mega-analysis", and highlight important considerations in the specification of each model and contrast. We compare the IBMA result to three CBMA methods: ALE (activation likelihood estimation), KDA (kernel density analysis) and MKDA (multi-level kernel density analysis), for various CBMA smoothing parameters. For the datasets considered, we find that ALE at sigma=15 mm, KDA at rho=25-30 mm and MKDA at rho=15 mm give the greatest similarity to the IBMA result, and that ALE was the most similar for this particular dataset, though only with a Dice similarity coefficient of 0.45 (Dice measure ranges from 0 to 1). Based on this poor similarity, and the greater modeling flexibility afforded by hierarchical mixed models, we suggest that IBMA is preferred over CBMA. To make IBMA analyses practical, however, the neuroimaging field needs to develop an effective mechanism for sharing image data, including whole-brain images of both effect estimates and their standard errors.},
author = {Salimi-Khorshidi, Gholamreza and Smith, Stephen M and Keltner, John R and Wager, Tor D and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2008.12.039},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Salimi-Khorshidi et al. - 2009 - Meta-analysis of neuroimaging data a comparison of image-based and coordinate-based pooling of studies.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Brain,Brain: physiology,Databases,Diagnostic Imaging,Diagnostic Imaging: methods,Diagnostic Imaging: standards,Factual,Humans,Meta-Analysis as Topic,meta-analysis},
mendeley-tags = {meta-analysis},
month = {apr},
number = {3},
pages = {810--23},
pmid = {19166944},
publisher = {Elsevier B.V.},
title = {{Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19166944},
volume = {45},
year = {2009}
}
@article{Weibull2008,
abstract = {This work addresses the balance between temporal signal-to-noise ratio (tSNR) and partial volume effects (PVE) in functional magnetic resonance imaging (fMRI) and investigates the impact of the choice of spatial resolution and smoothing. In fMRI, since physiological time courses are monitored, tSNR is of greater importance than image SNR. Improving SNR by an increase in voxel volume may be of negligible benefit when physiological fluctuations dominate the noise. Furthermore, at large voxel volumes, PVE are more pronounced, leading to an overall loss in performance. Artificial fMRI time series, based on high-resolution anatomical data, were used to simulate BOLD activation in a controlled manner. The performance was subsequently quantified as a measure of how well the resulted activation matched the simulated activation. The performance was highly dependent on the spatial resolution. At high contrast-to-noise ratio (CNR), the optimal voxel volume was small, i.e. in the region of 2(3) mm(3). It was also shown that using a substantially larger voxel volume in this case could potentially negate the CNR benefits. The optimal smoothing kernel width was dependent on the CNR, being larger at poor CNR. At CNR {\textgreater}1, little or no smoothing proved advantageous. The use of artificial time series gave an opportunity to quantitatively investigate the effects of partial volume and smoothing in single subject fMRI. It was shown that a proper choice of spatial resolution and smoothing kernel width is important for fMRI performance.},
author = {Weibull, a and Gustavsson, H and Mattsson, S and Svensson, J},
doi = {10.1016/j.neuroimage.2008.02.015},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Weibull et al. - 2008 - Investigation of spatial resolution, partial volume effects and smoothing in functional MRI using artificial 3D.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Artifacts,Brain,Brain: physiology,Computer-Assisted,Computer-Assisted: methods,Female,Humans,Image Processing,Magnetic Resonance Imaging,Models,Neurological,SNR,smoothing},
mendeley-tags = {SNR,smoothing},
month = {jun},
number = {2},
pages = {346--53},
pmid = {18400520},
title = {{Investigation of spatial resolution, partial volume effects and smoothing in functional MRI using artificial 3D time series.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18400520},
volume = {41},
year = {2008}
}
@article{LaBar2001,
abstract = {The impact of signal-to-noise (SNR) on fMRI of the amygdala was investigated during a picture encoding task. The SNR value required to observe reliable activation was determined by computer simulations. Blood oxygen level-dependent (BOLD) sensitivity maps were generated to indicate brain regions with sufficient SNR to test the statistical hypotheses. The results showed that the medial aspect of the amygdala had insufficient SNR to detect a 1{\%} peak BOLD signal change for a t-test comparison in a majority of subjects. None of these subjects showed activation in regions with unacceptable SNR values, indicating a low false positive rate. Furthermore, hemispheric asymmetries in the BOLD sensitivity maps mirrored asymmetries in the activation patterns. Impoverished SNR was also found in the basal forebrain and orbitofrontal cortex. These findings emphasize the importance of considering SNR when interpreting fMRI results in the limbic forebrain.},
author = {LaBar, K S and Gitelman, D R and Mesulam, M M and Parrish, T B},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/LaBar et al. - 2001 - Impact of signal-to-noise on functional MRI of the human amygdala.pdf:pdf},
issn = {0959-4965},
journal = {Neuroreport},
keywords = {Adult,Amygdala,Amygdala: physiology,Emotions,Emotions: physiology,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,SNR,Sensitivity and Specificity},
mendeley-tags = {SNR},
month = {nov},
number = {16},
pages = {3461--4},
pmid = {11733691},
title = {{Impact of signal-to-noise on functional MRI of the human amygdala.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11733691},
volume = {12},
year = {2001}
}
@article{Geissler2007,
abstract = {To evaluate the impact of data quality on the localization of brain activation in functional magnetic resonance imaging (fMRI) and to explore whether the temporal contrast-to-noise-ratio (CNR) provides a quantitative parameter to estimate fMRI quality.},
author = {Geissler, Alexander and Gartus, Andreas and Foki, Thomas and Tahamtan, Amir Reza and Beisteiner, Roland and Barth, Markus},
doi = {10.1002/jmri.20935},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Geissler et al. - 2007 - Contrast-to-noise ratio (CNR) as a quality parameter in fMRI.pdf:pdf},
issn = {1053-1807},
journal = {Journal of magnetic resonance imaging : JMRI},
keywords = {Adult,Brain Diseases,Brain Diseases: pathology,Brain Mapping,Brain Mapping: methods,CNR,Computer-Assisted,Data Interpretation,Female,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Quality Control,SNR,Signal Processing,Statistical},
mendeley-tags = {CNR,SNR},
month = {jun},
number = {6},
pages = {1263--70},
pmid = {17520733},
title = {{Contrast-to-noise ratio (CNR) as a quality parameter in fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17520733},
volume = {25},
year = {2007}
}
@article{Murphy2007,
author = {Murphy, Kevin and Bodurka, Jerzy and Bandettini, Peter A.},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Murphy, Bodurka, Bandettini - 2007 - How long to scan The relationship between fMRI temporal signal to noise and necessary scan duration.pdf:pdf},
journal = {Neuroimage},
number = {2},
pages = {565--574},
title = {{How long to scan? The relationship between fMRI temporal signal to noise and necessary scan duration}},
volume = {34},
year = {2007}
}
@article{Kriegeskorte2006,
abstract = {The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.},
author = {Kriegeskorte, Nikolaus and Goebel, Rainer and Bandettini, Peter A.},
doi = {10.1073/pnas.0600244103},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Kriegeskorte, Goebel, Bandettini - 2006 - Information-based functional brain mapping.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Adolescent,Adult,Brain Mapping,Brain Mapping: methods,Computer Simulation,Data Interpretation,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Magnetic Resonance Imaging: statistics {\&} numerical,Male,Models,Multivariate Analysis,Neurological,Statistical},
month = {mar},
number = {10},
pages = {3863--8},
pmid = {16537458},
title = {{Information-based functional brain mapping.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1383651{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {103},
year = {2006}
}
@article{Schweder1982,
author = {Schweder, T. and Spjotvoll, E},
doi = {10.2307/2335984},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Schweder, Spjotvoll - 1982 - Plots of P-Values to Evaluate Many Tests Simultaneously.pdf:pdf},
issn = {00063444},
journal = {Biometrika},
keywords = {pi0},
mendeley-tags = {pi0},
month = {dec},
number = {3},
pages = {493},
title = {{Plots of P-Values to Evaluate Many Tests Simultaneously}},
url = {http://www.jstor.org/stable/2335984?origin=crossref},
volume = {69},
year = {1982}
}
@article{Darki2009,
abstract = {Functional magnetic resonance imaging (fMRI) is an effective method for measuring the brain neuronal activities. Numerous statistical methods are used for fMRI analyzing. However, determining the true activated regions among the whole apparent activated voxels is a vital but challenging task. The activation pattern of fMRI data analysis is affected under the presence of source of variations such as noise, artifacts, and physiological fluctuations. Finding an accurate and reliable activation map from a single data analysis is essential for true interpretation of an individual data especially when it should be used in neurosurgical planning. We introduced a resampling process (called Bootstrapping) through the original EPI data, with the aim of evaluating the reproducibility of the activation changes throughout a task-related signal variation.},
author = {Darki, F and Oghabian, M a},
doi = {10.1109/IEMBS.2009.5334207},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Darki, Oghabian - 2009 - Accurate activation map detection using bootstrap resampling of single fMRI data.pdf:pdf},
isbn = {9781424432967},
issn = {1557-170X},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,bootstrap},
mendeley-tags = {bootstrap},
month = {jan},
pages = {4783--6},
pmid = {19964851},
title = {{Accurate activation map detection using bootstrap resampling of single fMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19964851},
volume = {2009},
year = {2009}
}
@article{Hsueh2003,
abstract = {When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.},
author = {Hsueh, Huey-miin and Chen, James J and Kodell, Ralph L},
doi = {10.1081/BIP-120024202},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Hsueh, Chen, Kodell - 2003 - Comparison of methods for estimating the number of true null hypotheses in multiplicity testing.pdf:pdf},
issn = {1054-3406},
journal = {Journal of biopharmaceutical statistics},
keywords = {Animals,Likelihood Functions,Models,Probability,Rats,Statistical,Statistics as Topic,evaluation,pi0},
mendeley-tags = {evaluation,pi0},
month = {nov},
number = {4},
pages = {675--89},
pmid = {14584715},
title = {{Comparison of methods for estimating the number of true null hypotheses in multiplicity testing.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/14584715},
volume = {13},
year = {2003}
}
@article{Rorden2007,
abstract = {Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency--for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects.},
author = {Rorden, Chris and Bonilha, Leonardo and Nichols, Thomas E},
doi = {10.1016/j.neuroimage.2006.12.043},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Rorden, Bonilha, Nichols - 2007 - Rank-order versus mean based statistics for neuroimaging.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain: pathology,Computer Simulation,Computer-Assisted,Computer-Assisted: statistics {\&} numerical data,Data Interpretation,Epilepsy,False Positive Reactions,Hippocampus,Hippocampus: pathology,Humans,Image Processing,MTP,Magnetic Resonance Imaging,Magnetic Resonance Imaging: statistics {\&} numerical,Nonparametric,Sample Size,Statistical,Statistics,Temporal Lobe,Temporal Lobe: pathology,fMRI},
mendeley-tags = {MTP,fMRI},
month = {may},
number = {4},
pages = {1531--7},
pmid = {17391987},
title = {{Rank-order versus mean based statistics for neuroimaging.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17391987},
volume = {35},
year = {2007}
}
@article{Nguyen2004,
author = {Nguyen, D},
doi = {10.1016/j.csda.2004.01.006},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nguyen - 2004 - On estimating the proportion of true null hypotheses for false discovery rate controlling procedures in exploratory DNA.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics {\&} Data Analysis},
keywords = {di erential gene expression,dna microarray,false discovery rate,multiple hypothesis testing,pi0},
mendeley-tags = {pi0},
month = {oct},
number = {3},
pages = {611--637},
title = {{On estimating the proportion of true null hypotheses for false discovery rate controlling procedures in exploratory DNA microarray studies}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0167947304000040},
volume = {47},
year = {2004}
}
@article{Nandy2003,
abstract = {The receiver operating characteristic (ROC) method is a useful and popular tool for testing the efficiency of various diagnostic tests applicable to functional MRI (fMRI) data. Typically, the diagnostic tests are applied on simulated and pseudo-human fMRI data, and the area under the ROC curve is used as a measure of the efficiency of the diagnostic test. The effectiveness of such a method depends on how well the simulated data approximate the real data. For multivariate statistical methods, however, this technique is usually inadequate, as the spatial dependence among voxels is ignored for simulated data. In this work a modified ROC method using real fMRI data with a broader scope is proposed. This method can be applied to most fMRI postprocessing techniques, including multivariate analyses such as canonical correlation analysis (CCA). Also, the relationship of the modified ROC method with the conventional ROC method is discussed in detail.},
author = {Nandy, Rajesh R and Cordes, Dietmar},
doi = {10.1002/mrm.10469},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nandy, Cordes - 2003 - Novel ROC-type method for testing the efficiency of multivariate statistical methods in fMRI.pdf:pdf},
issn = {0740-3194},
journal = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
keywords = {Algorithms,Brain,Brain: anatomy {\&} histology,Computer-Assisted,Fourier Analysis,Humans,Image Processing,Magnetic Resonance Imaging,Multivariate Analysis,ROC,ROC Curve,multivariate},
mendeley-tags = {ROC,multivariate},
month = {jun},
number = {6},
pages = {1152--62},
pmid = {12768594},
title = {{Novel ROC-type method for testing the efficiency of multivariate statistical methods in fMRI.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12768594},
volume = {49},
year = {2003}
}
@article{Pounds2003,
author = {Pounds, S. and Morris, S. W.},
doi = {10.1093/bioinformatics/btg148},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Pounds, Morris - 2003 - Estimating the occurrence of false positives and false negatives in microarray studies by approximating and part.pdf:pdf},
issn = {1367-4803},
journal = {Bioinformatics},
keywords = {balance,bayes,pi0},
month = {jul},
number = {10},
pages = {1236--1242},
title = {{Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values}},
url = {http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btg148},
volume = {19},
year = {2003}
}
@article{Langers2007,
abstract = {In the context of neuroimaging experiments, it is essential to account for the multiple comparisons problem when thresholding statistical mappings. Various methods are in use to deal with this issue, but they differ in their signal detection power for small- and large-scale effects. In this paper, we comprehensively describe a new method that is based on control of the false discovery rate (FDR). Our method increases sensitivity by exploiting the spatially clustered nature of neuroimaging effects. This is achieved by using a sliding window technique, in which FDR-control is first applied at a regional level. Thus, a new statistical map that is related to the regionally achieved FDR is derived from the available voxelwise P-values. On the basis of receiver operating characteristic (ROC) curves, thresholding based on this map is demonstrated to have better discriminatory power than conventional thresholding based on P-values. Secondly, it is shown that the resulting maps can be thresholded at a level that results in control of the global FDR. By means of statistical arguments and numerical simulations under widely varying conditions, our method is validated, characterized, and compared to some other common voxel-based methods (uncorrected thresholding, Bonferroni correction, and conventional FDR-control). It is found that our method shows considerably higher sensitivity as compared to conventional FDR-control, while still controlling the achieved FDR at the same level or better. Finally, our method is applied to two diverse neuroimaging experiments to assess its practical merits, resulting in substantial improvements as compared to the other methods.},
author = {Langers, Dave R M and Jansen, Jacobus F a and Backes, Walter H},
doi = {10.1016/j.neuroimage.2007.07.031},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Langers, Jansen, Backes - 2007 - Enhanced signal detection in neuroimaging by means of regional control of the global false discovery ra.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Adult,Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Computer-Assisted,Computer-Assisted: methods,Evoked Potentials,Evoked Potentials: physiology,FDR,False Positive Reactions,Female,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Reproducibility of Results,Sensitivity and Specificity,Signal Processing},
mendeley-tags = {FDR},
month = {oct},
number = {1},
pages = {43--56},
pmid = {17825583},
title = {{Enhanced signal detection in neuroimaging by means of regional control of the global false discovery rate.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17825583},
volume = {38},
year = {2007}
}
@article{PeronePacifico2004,
author = {{Perone Pacifico}, M and Genovese, C and Verdinelli, I and Wasserman, L},
doi = {10.1198/0162145000001655},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Perone Pacifico et al. - 2004 - False Discovery Control for Random Fields.pdf:pdf},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {FDR,RFT,topological inference},
mendeley-tags = {FDR,RFT,topological inference},
month = {dec},
number = {468},
pages = {1002--1014},
title = {{False Discovery Control for Random Fields}},
url = {http://pubs.amstat.org/doi/abs/10.1198/0162145000001655},
volume = {99},
year = {2004}
}
@article{Genovese2002a,
author = {Genovese, Christopher and Wasserman, Larry},
doi = {10.1111/1467-9868.00347},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Genovese, Wasserman - 2002 - Operating characteristics and extensions of the false discovery rate procedure.pdf:pdf},
issn = {13697412},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
keywords = {FDR,ROC},
mendeley-tags = {FDR,ROC},
month = {aug},
number = {3},
pages = {499--517},
title = {{Operating characteristics and extensions of the false discovery rate procedure}},
url = {http://doi.wiley.com/10.1111/1467-9868.00347},
volume = {64},
year = {2002}
}
@article{Forman1995,
author = {Forman, Steven D. and Cohen, Jonathan D. and Fitzgerald, Mark and Eddy, William F. and Mintun, Mark A. and Noll, Douglas C.},
doi = {10.1002/mrm.1910330508},
issn = {07403194},
journal = {Magnetic Resonance in Medicine},
month = {may},
number = {5},
pages = {636--647},
title = {{Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster-Size Threshold}},
url = {http://doi.wiley.com/10.1002/mrm.1910330508},
volume = {33},
year = {1995}
}
@article{lindquist2008statistical,
author = {Lindquist, M.A.},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Lindquist - 2008 - The statistical analysis of fMRI data.pdf:pdf},
journal = {Statistical Science},
keywords = {MTP,fMRI},
mendeley-tags = {fMRI},
number = {4},
pages = {439--464},
publisher = {Institute of Mathematical Statistics},
title = {{The statistical analysis of fMRI data}},
url = {http://projecteuclid.org/euclid.ss/1242049389},
volume = {23},
year = {2008}
}
@article{Zhang2008a,
author = {Zhang, Chunming and Lu, Yuefeng and Johnstone, Tom and Oakes, Terry and Davidson, Richard J.},
doi = {10.1016/j.csda.2008.03.033},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2008 - Efficient modeling and inference for event-related fMRI data.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics {\&} Data Analysis},
keywords = {MTP},
month = {jun},
number = {10},
pages = {4859--4871},
title = {{Efficient modeling and inference for event-related fMRI data}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S016794730800203X},
volume = {52},
year = {2008}
}
@article{Wang2009,
abstract = {There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral F hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.},
author = {Wang, Yongmei Michelle and Xia, Jing},
doi = {10.1109/TMI.2009.2014863},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Wang, Xia - 2009 - Unified framework for robust estimation of brain networks from FMRI using temporal and spatial correlation analyses.pdf:pdf},
issn = {1558-0062},
journal = {IEEE transactions on medical imaging},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Computer Simulation,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Normal Distribution,ROC Curve,Time Factors,networks,spatial},
mendeley-tags = {networks,spatial},
month = {aug},
number = {8},
pages = {1296--307},
pmid = {19237342},
title = {{Unified framework for robust estimation of brain networks from FMRI using temporal and spatial correlation analyses.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19237342},
volume = {28},
year = {2009}
}
@article{Feilner2005,
abstract = {We present a new family of two-dimensional and three-dimensional orthogonal wavelets which uses quincunx sampling. The orthogonal refinement filters have a simple analytical expression in the Fourier domain as a function of the order lamda, which may be noninteger. We can also prove that they yield wavelet bases of L2(R2) for any lambda {\textgreater} 0. The wavelets are fractional in the sense that the approximation error at a given scale a decays like O(a(lamda)); they also essentially behave like fractional derivative operators. To make our construction practical, we propose a fast Fourier transform-based implementation that turns out to be surprisingly fast. In fact, our method is almost as efficient as the standard Mallat algorithm for separable wavelets.},
author = {Feilner, Manuela and van de Ville, Dimitri},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Feilner, van de Ville - 2005 - An orthogonal family of quincunx wavelets with continuously adjustable order.pdf:pdf},
issn = {1057-7149},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer-Assisted,Computer-Assisted: methods,Image Enhancement,Image Enhancement: methods,Image Interpretation,Information Storage and Retrieval,Information Storage and Retrieval: methods,Numerical Analysis,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Signal Processing,wavelets},
mendeley-tags = {wavelets},
month = {apr},
number = {4},
pages = {499--510},
pmid = {15825484},
title = {{An orthogonal family of quincunx wavelets with continuously adjustable order.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15825484},
volume = {14},
year = {2005}
}
@article{Sun2009,
author = {Sun, Wenguang and {Tony Cai}, T.},
doi = {10.1111/j.1467-9868.2008.00694.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Sun, Tony Cai - 2009 - Large-scale multiple testing under dependence.pdf:pdf},
issn = {13697412},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
keywords = {MTP,compound decision problem,dependence,false discovery rate,hidden markov models,local,multiple testing under dependence,significance index},
mendeley-tags = {MTP,dependence},
month = {apr},
number = {2},
pages = {393--424},
title = {{Large-scale multiple testing under dependence}},
url = {http://doi.wiley.com/10.1111/j.1467-9868.2008.00694.x},
volume = {71},
year = {2009}
}
@article{Schwartzman2009,
abstract = {Current strategies for thresholding statistical parametric maps in neuroimaging include control of the family-wise error rate, control of the false discovery rate (FDR) and thresholding of the posterior probability of a voxel being active given the data, the latter derived from a mixture model of active and inactive voxels. Correct inference using any of these criteria depends crucially on the specification of the null distribution of the test statistics. In this article we show examples from fMRI and DTI data where the theoretical null distribution does not match well the observed distribution of the test statistics. As a solution, we introduce the use of an empirical null, a null distribution empirically estimated from the data itself, allowing for global corrections of theoretical null assumptions. The theoretical null distributions considered are normal, t, chi(2) and F, all commonly encountered in neuroimaging. The empirical null estimate is accompanied by an estimate of the proportion of non-active voxels in the data. Based on the two-class mixture model, we present the equivalence between the strategies of controlling FDR and thresholding posterior probabilities in the context of neuroimaging and show that the FDR estimates derived from the empirical null can be seen as empirical Bayes estimates.},
author = {Schwartzman, Armin and Dougherty, Robert F and Lee, Jongho and Ghahremani, Dara and Taylor, Jonathan E},
doi = {10.1016/j.neuroimage.2008.04.182},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Schwartzman et al. - 2009 - Empirical null and false discovery rate analysis in neuroimaging(2).pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Artifacts,Brain,Brain Mapping,Brain Mapping: methods,Brain: radionuclide imaging,Computer-Assisted,Computer-Assisted: methods,FDR,Humans,Image Processing,Imaging,Three-Dimensional,bayes},
mendeley-tags = {FDR,bayes},
month = {jan},
number = {1},
pages = {71--82},
pmid = {18547821},
publisher = {Elsevier Inc.},
title = {{Empirical null and false discovery rate analysis in neuroimaging.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18547821},
volume = {44},
year = {2009}
}
@article{Sarkar2009,
author = {Sarkar, Sanat K. and Guo, Wenge},
doi = {10.1214/08-AOS617},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Sarkar, Guo - 2009 - On a generalized false discovery rate.pdf:pdf},
issn = {0090-5364},
journal = {The Annals of Statistics},
keywords = {FDR,and phrases,average power,bayes,gene expression,generalized,generalized fdr},
mendeley-tags = {FDR,bayes},
month = {jun},
number = {3},
pages = {1545--1565},
title = {{On a generalized false discovery rate}},
url = {http://projecteuclid.org/euclid.aos/1239369031},
volume = {37},
year = {2009}
}
@article{Pendse2009a,
abstract = {A typical fMRI data analysis proceeds via the generalized linear model (GLM) with Gaussian noise using a model based on the experimental paradigm. This analysis ultimately results in the production of z-statistic images corresponding to the contrasts of interest. Thresholding such z-statistic images at uncorrected thresholds suitable for testing activation at a single voxel results in the problem of multiple comparisons. A number of methods which account for the problem of multiple comparisons have been proposed including Gaussian random field theory, mixture modeling and false discovery rate (FDR). The focus of this paper is on the development of a generalized version of FDR (GFDR) in an empirical Bayesian framework, specially adapted for fMRI thresholding, that is more robust to modeling violations as compared to traditional FDR. We show theoretically as well as by simulation that for real fMRI data various factors lead to a mixture of Gaussians (MOG) density for the "null" distribution. Artificial data was used to systematically study the bias of FDR and GFDR under varying intensity of modeling violations, signal to noise ratios and activation fractions for a range of q values. GFDR was able to handle modeling violations and produce good results when FDR failed. Real fMRI data was also used to confirm GFDR capabilities. Our results indicate that it is very important to account for the form and fraction of the "null" hypothesis adaptively from the data in order to obtain valid inference.},
author = {Pendse, Gautam and Borsook, David and Becerra, Lino},
doi = {10.1016/j.neuroimage.2009.02.035},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Pendse, Borsook, Becerra - 2009 - Enhanced false discovery rate using Gaussian mixture models for thresholding fMRI statistical maps(2).pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Bayes Theorem,Brain,Brain: physiology,Computer Simulation,Computer-Assisted,FDR,Humans,Likelihood Functions,Linear Models,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Normal Distribution,Signal Processing,Theoretical,bayes},
mendeley-tags = {FDR,bayes},
month = {aug},
number = {1},
pages = {231--61},
pmid = {19269334},
publisher = {Elsevier Inc.},
title = {{Enhanced false discovery rate using Gaussian mixture models for thresholding fMRI statistical maps.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2735129{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {47},
year = {2009}
}
@article{Nichols2002,
abstract = {Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.},
author = {Nichols, Thomas E and Holmes, Andrew P},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Nichols, Holmes - 2002 - Nonparametric permutation tests for functional neuroimaging a primer with examples.pdf:pdf},
issn = {1065-9471},
journal = {Human brain mapping},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: radionuclide imaging,Computer-Assisted,Computer-Assisted: methods,Data Interpretation,Emission-Computed,Emission-Computed: methods,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Signal Processing,Statistical,Statistical Distributions,Tomography,resampling},
mendeley-tags = {resampling},
month = {jan},
number = {1},
pages = {1--25},
pmid = {11747097},
title = {{Nonparametric permutation tests for functional neuroimaging: a primer with examples.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11747097},
volume = {15},
year = {2002}
}
@article{Moerkerke2006,
author = {Moerkerke, Beatrijs and Goetghebeur, E. and {De Riek}, J. and Roldan-Ruiz, I.},
doi = {10.1111/j.1467-985X.2005.00390.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Moerkerke et al. - 2006 - Significance and impotence towards a balanced view of the null and the alternative hypotheses in marker select.pdf:pdf},
issn = {0964-1998},
journal = {Journal of the Royal Statistical Society: Series A (Statistics in Society)},
keywords = {alternative p-value,assisted plant breeding,balance,genetic association,genetic marker selection,marker-,sequential marker selection},
mendeley-tags = {balance},
month = {jan},
number = {1},
pages = {61--79},
title = {{Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding}},
url = {http://doi.wiley.com/10.1111/j.1467-985X.2005.00390.x},
volume = {169},
year = {2006}
}
@article{Maitra2009,
abstract = {Functional Magnetic Resonance Imaging (fMRI) is widely used to study activation in the human brain. In most cases, data are commonly used to construct activation maps corresponding to a given paradigm. Results can be very variable, hence quantifying certainty of identified activation and inactivation over studies is important. This paper provides a model-based approach to certainty estimation from data acquired over several replicates of the same experimental paradigm. Specifically, the p-values derived from the statistical analysis of the data are explicitly modeled as a mixture of their underlying distributions; thus, unlike the methodology currently in use, there is no subjective thresholding required in the estimation process. The parameters governing the mixture model are easily obtained by the principle of maximum likelihood. Further, the estimates can also be used to optimally identify voxel-specific activation regions along with their corresponding certainty measures. The methodology is applied to a study involving a motor paradigm performed on a single subject several times over a period of two months. Simulation experiments used to calibrate performance of the method are promising. The methodology is also seen to be robust in determining areas of activation and their corresponding certainties.},
author = {Maitra, Ranjan},
doi = {10.1016/j.neuroimage.2009.03.073},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Maitra - 2009 - Assessing certainty of activation or inactivation in test-retest fMRI studies.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain: physiology,Computer Simulation,Computer-Assisted,Hand,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Motor Activity,Motor Activity: physiology,Signal Processing,stability},
mendeley-tags = {stability},
month = {aug},
number = {1},
pages = {88--97},
pmid = {19361562},
publisher = {Elsevier Inc.},
title = {{Assessing certainty of activation or inactivation in test-retest fMRI studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19361562},
volume = {47},
year = {2009}
}
@article{Lindquist2009,
author = {Lindquist, Martin a. and Gelman, Andrew},
doi = {10.1111/j.1745-6924.2009.01130.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Lindquist, Gelman - 2009 - Correlations and Multiple Comparisons in Functional Imaging A Statistical Perspective (Commentary on Vul et a.pdf:pdf},
issn = {17456916},
journal = {Perspectives on Psychological Science},
keywords = {MTP,fMRI},
mendeley-tags = {MTP,fMRI},
month = {may},
number = {3},
pages = {310--313},
title = {{Correlations and Multiple Comparisons in Functional Imaging: A Statistical Perspective (Commentary on Vul et al., 2009)}},
url = {http://pps.sagepub.com/lookup/doi/10.1111/j.1745-6924.2009.01130.x},
volume = {4},
year = {2009}
}
@article{Qi1999,
abstract = {We examine the spatial resolution and variance properties of PET images reconstructed using maximum a posteriori (MAP) or penalized-likelihood methods. Resolution is characterized by the contrast recovery coefficient (CRC) of the local impulse response. Simplified approximate expressions are derived for the local impulse response CRC's and variances for each voxel. Using these results we propose a practical scheme for selecting spatially variant smoothing parameters to optimize lesion detectability through maximization of the local CRC-to-noise ratio in the reconstructed image.},
author = {Qi, J and Leahy, R M},
doi = {10.1109/42.768839},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Qi, Leahy - 1999 - A theoretical study of the contrast recovery and variance of MAP reconstructions from PET data.pdf:pdf},
issn = {0278-0062},
journal = {IEEE transactions on medical imaging},
keywords = {Analysis of Variance,Animals,Models, Theoretical,Normal Distribution,Tomography, Emission-Computed},
month = {apr},
number = {4},
pages = {293--305},
pmid = {10385287},
title = {{A theoretical study of the contrast recovery and variance of MAP reconstructions from PET data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10385287},
volume = {18},
year = {1999}
}
@article{Heller2006a,
abstract = {We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation. The advantages of this analysis over previous ones are both conceptual and statistical. Recognizing that the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data. Testing the cluster units has a two-fold statistical advantage over testing each voxel separately: the signal to noise ratio within the unit tested is higher, and the number of hypotheses tests compared is smaller. We suggest controlling FDR on clusters, i.e., the proportion of clusters rejected erroneously out of all clusters rejected and explain the meaning of controlling this error rate. We introduce the powerful adaptive procedure to control the FDR on clusters. We apply our cluster-based analysis (CBA) to both an event-related and a block design fMRI vision experiment and demonstrate its increased power over voxel-by-voxel analysis in these examples as well as in simulations.},
author = {Heller, Ruth and Stanley, Damian and Yekutieli, Daniel and Rubin, Nava and Benjamini, Yoav},
doi = {10.1016/j.neuroimage.2006.04.233},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Heller et al. - 2006 - Cluster-based analysis of FMRI data.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Cluster Analysis,FDR,Functional Laterality,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models,Neurological,Occipital Lobe,Occipital Lobe: anatomy {\&} histology,topological inference},
mendeley-tags = {FDR,topological inference},
month = {nov},
number = {2},
pages = {599--608},
pmid = {16952467},
title = {{Cluster-based analysis of FMRI data.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16952467},
volume = {33},
year = {2006}
}
@article{Guindani2009,
abstract = {We discuss a Bayesian discovery procedure for multiple comparison problems. We show that under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule based on a threshold of the posterior probability of the alternative. Under a semi-parametric model for the data, we show that the Bayes rule can be approximated by the optimal discovery procedure (ODP), recently introduced by Storey (2007a). Improving the approximation leads us to a Bayesian discovery procedure (BDP), which exploits the multiple shrinkage in clusters implied by the assumed nonparametric model. We compare the BDP and the ODP estimates in a simple simulation study and in an assessment of differential gene expression based on microarray data from tumor samples. We extend the setting of the ODP by discussing modifications of the loss function that lead to different single thresholding statistics. Finally, we provide an application of the previous arguments to dependent (spatial) data.},
author = {Guindani, Michele and M{\"{u}}ller, Peter and Zhang, Song},
doi = {10.1111/j.1467-9868.2009.00714.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Guindani, M{\"{u}}ller, Zhang - 2009 - A Bayesian Discovery Procedure.pdf:pdf},
issn = {1369-7412},
journal = {Journal of the Royal Statistical Society. Series B, Statistical methodology},
keywords = {bayes,bayes optimal rule,false discovery rate,loss function,multiple comparison},
mendeley-tags = {bayes},
month = {nov},
number = {5},
pages = {905--925},
pmid = {20694043},
title = {{A Bayesian Discovery Procedure.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2914327{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {71},
year = {2009}
}
@article{Liu1998,
abstract = {The goal of our research is to develop an experimental and analytical framework for spatiotemporal imaging of human brain function. Preliminary studies suggest that noninvasive spatiotemporal maps of cerebral activity can be produced by combining the high spatial resolution (millimeters) of functional MRI (fMRI) with the high temporal resolution (milliseconds) of electroencephalography (EEG) and magnetoencephalography (MEG). Although MEG and EEG are sensitive to millisecond changes in mental activity, the ability to resolve source localization and timing is limited by the ill-posed "inverse" problem. We conducted Monte Carlo simulations to evaluate the use of MRI constraints in a linear estimation inverse procedure, where fMRI weighting, cortical location and orientation, and sensor noise statistics were realistically incorporated. An error metric was computed to quantify the effects of fMRI invisible ("missing") sources, "extra" fMRI sources, and cortical orientation errors. Our simulation results demonstrate that prior anatomical and functional information from MRI can be used to regularize the EEG/MEG inverse problem, giving an improved solution with high spatial and temporal resolution. An fMRI weighting of approximately 90{\%} was determined to provide the best compromise between separation of activity from correctly localized sources and minimization of error caused by missing sources. The accuracy of the estimate was relatively independent of the number and extent of the sources, allowing for incorporation of physiologically realistic multiple distributed sources. This linear estimation method provides an operator-independent approach for combining information from fMRI, MEG, and EEG and represents a significant advance over traditional dipole modeling.},
author = {Liu, a K and Belliveau, J W and Dale, a M},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Liu, Belliveau, Dale - 1998 - Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography dat.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Brain,Brain: physiopathology,Brain: radiography,Humans,Magnetic Resonance Imaging,Magnetoencephalography,Monte Carlo Method,simulations},
mendeley-tags = {simulations},
month = {jul},
number = {15},
pages = {8945--50},
pmid = {9671784},
title = {{Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=21182{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {95},
year = {1998}
}
@article{Farcomeni2009,
author = {Farcomeni, Alessio},
doi = {10.1111/j.1467-9469.2008.00633.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Farcomeni - 2009 - Generalized Augmentation to Control the False Discovery Exceedance in Multiple Testing.pdf:pdf},
issn = {03036898},
journal = {Scandinavian Journal of Statistics},
keywords = {dependence,dna microarrays,false discovery exceedance,false discovery rate,family-wise error rate,multiple testing},
month = {sep},
number = {3},
pages = {501--517},
title = {{Generalized Augmentation to Control the False Discovery Exceedance in Multiple Testing}},
url = {http://doi.wiley.com/10.1111/j.1467-9469.2008.00633.x},
volume = {36},
year = {2009}
}
@article{Farcomeni2008,
abstract = {In the last decade a growing amount of statistical research has been devoted to multiple testing, motivated by a variety of applications in medicine, bioinformatics, genomics, brain imaging, etc. Research in this area is focused on developing powerful procedures even when the number of tests is very large. This paper attempts to review research in modern multiple hypothesis testing with particular attention to the false discovery proportion, loosely defined as the number of false rejections divided by the number of rejections. We review the main ideas, stepwise and augmentation procedures; and resampling based testing. We also discuss the problem of dependence among the test statistics. Simulations make a comparison between the procedures and with Bayesian methods. We illustrate the procedures in applications in DNA microarray data analysis. Finally, few possibilities for further research are highlighted.},
author = {Farcomeni, Alessio},
doi = {10.1177/0962280206079046},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Farcomeni - 2008 - A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion.pdf:pdf},
issn = {0962-2802},
journal = {Statistical methods in medical research},
keywords = {Bayes Theorem,FDP,FDR,False Positive Reactions,MTP,Models,Theoretical,evaluation},
mendeley-tags = {FDP,FDR,MTP,evaluation},
month = {aug},
number = {4},
pages = {347--88},
pmid = {17698936},
title = {{A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17698936},
volume = {17},
year = {2008}
}
@article{Efron1996,
author = {Efron, Bradley},
doi = {10.2307/2291646},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Efron - 1996 - Empirical Bayes Methods for Combining Likelihoods.pdf:pdf},
issn = {01621459},
journal = {Journal of the American Statistical Association},
keywords = {bayes},
mendeley-tags = {bayes},
month = {jun},
number = {434},
pages = {538},
title = {{Empirical Bayes Methods for Combining Likelihoods}},
url = {http://www.jstor.org/stable/2291646?origin=crossref},
volume = {91},
year = {1996}
}
@article{Delongchamp2004,
abstract = {An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ability of the study to resolve effects. In our work, we are more interested in selecting most of the affected genes rather than protecting against a few false positives. An optimum cutoff, i.e., the best set given the data, depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. We select the cutoff by a decision-theoretic method analogous to methods developed for receiver operating characteristic curves. In addition, we estimate the false discovery rate and the false nondiscovery rate associated with any cutoff value. Two functional genomics studies that were designed to assess a treatment effect are used to illustrate how the methods allowed the investigators to determine a cutoff to suit their research goals.},
author = {Delongchamp, Robert R and Bowyer, John F and Chen, James J and Kodell, Ralph L},
doi = {10.1111/j.0006-341X.2004.00228.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Delongchamp et al. - 2004 - Multiple-testing strategy for analyzing cDNA array data on gene expression.pdf:pdf},
issn = {0006-341X},
journal = {Biometrics},
keywords = {Amphetamine,Amphetamine: pharmacology,Animals,Biometry,Brain,Brain: drug effects,Brain: metabolism,Data Interpretation,Gene Expression,Gene Expression Profiling,Gene Expression Profiling: statistics {\&} numerical,Gene Expression: drug effects,Genomics,Genomics: statistics {\&} numerical data,Models,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: statistic,ROC Curve,Rats,Statistical,balance,pi0},
mendeley-tags = {balance,pi0},
month = {sep},
number = {3},
pages = {774--82},
pmid = {15339301},
title = {{Multiple-testing strategy for analyzing cDNA array data on gene expression.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15339301},
volume = {60},
year = {2004}
}
@article{Broberg2005,
abstract = {In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by pure chance. The distribution of these p-values is a mixture of two components corresponding to the changed genes and the unchanged ones. The focus of this article is how to estimate the proportion unchanged and the false discovery rate (FDR) and how to make inferences based on these concepts. Six published methods for estimating the proportion unchanged genes are reviewed, two alternatives are presented, and all are tested on both simulated and real data. All estimates but one make do without any parametric assumptions concerning the distributions of the p-values. Furthermore, the estimation and use of the FDR and the closely related q-value is illustrated with examples. Five published estimates of the FDR and one new are presented and tested. Implementations in R code are available.},
author = {Broberg, Per},
doi = {10.1186/1471-2105-6-199},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Broberg - 2005 - A comparative review of estimates of the proportion unchanged genes and the false discovery rate.pdf:pdf},
isbn = {1471210561},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Amino Acid Sequence,Amino Acid Sequence: genetics,Base Sequence,Base Sequence: genetics,Computational Biology,Computational Biology: methods,DNA,DNA: genetics,False Positive Reactions,Gene Order,Genetic,Genetic: genetics,Microarray Analysis,Microarray Analysis: methods,Models,Molecular,Templates,Transcription,evaluation,pi0},
mendeley-tags = {evaluation,pi0},
month = {jan},
pages = {199},
pmid = {16086831},
title = {{A comparative review of estimates of the proportion unchanged genes and the false discovery rate.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1199583{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2005}
}
@article{Bennett2010,
abstract = {Functional magnetic resonance imaging (fMRI) is one of the most important methods for in vivo investigation of cognitive processes in the human brain. Within the last two decades, an explosion of research has emerged using fMRI, revealing the underpinnings of everything from motor and sensory processes to the foundations of social cognition. While these results have revealed the potential of neuroimaging, important questions regarding the reliability of these results remain unanswered. In this paper, we take a close look at what is currently known about the reliability of fMRI findings. First, we examine the many factors that influence the quality of acquired fMRI data. We also conduct a review of the existing literature to determine if some measure of agreement has emerged regarding the reliability of fMRI. Finally, we provide commentary on ways to improve fMRI reliability and what questions remain unanswered. Reliability is the foundation on which scientific investigation is based. How reliable are the results from fMRI?},
author = {Bennett, Craig M and Miller, Michael B},
doi = {10.1111/j.1749-6632.2010.05446.x},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bennett, Miller - 2010 - How reliable are the results from functional magnetic resonance imaging.pdf:pdf},
issn = {1749-6632},
journal = {Annals of the New York Academy of Sciences},
keywords = {Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Reproducibility of Results,stability},
mendeley-tags = {stability},
month = {mar},
pages = {133--55},
pmid = {20392279},
title = {{How reliable are the results from functional magnetic resonance imaging?}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20392279},
volume = {1191},
year = {2010}
}
@inproceedings{Seghouane2010,
author = {Seghouane, Abd-Krim and Ong, Ju Lynn},
booktitle = {2010 IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2010.5653354},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Seghouane, Ong - 2010 - A Bayesian model selection approach to fMRI activation detection.pdf:pdf},
isbn = {978-1-4244-7992-4},
keywords = {bayes},
mendeley-tags = {bayes},
month = {sep},
pages = {4401--4404},
publisher = {IEEE},
title = {{A Bayesian model selection approach to fMRI activation detection}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5653354},
year = {2010}
}
@article{Allison2002,
author = {Allison, David B. and Gadbury, Gary L. and Heo, Moonseong and Fernandez, Jose R. and Lee, Cheol-Koo and Prolla, Tomas A. and Weindruch, Richard},
doi = {10.1016/S0167-9473(01)00046-9},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Allison et al. - 2002 - A mixture model approach for the analysis of microarray gene expression data.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics {\&} Data Analysis},
keywords = {microarray,pi0},
mendeley-tags = {microarray,pi0},
month = {mar},
number = {1},
pages = {1--20},
title = {{A mixture model approach for the analysis of microarray gene expression data}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0167947301000469},
volume = {39},
year = {2002}
}
@article{Bowman2005a,
abstract = {Functional neuroimaging, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), plays an important role in identifying specific brain regions associated with experimental stimuli or psychiatric disorders such as schizophrenia. PET and fMRI produce massive data sets that contain both temporal correlations from repeated scans and complex spatial correlations. Several methods exist for handling temporal correlations, some of which rely on transforming the response data to induce either a known or an independence covariance structure. Despite the presence of spatial correlations between the volume elements (voxels) comprising a brain scan, conventional methods perform voxel-by-voxel analyses of measured brain activity. We propose a two-stage spatio-temporal model for the estimation and testing of localized activity. Our second-stage model specifies a spatial auto-regression, capturing correlations within neural processing clusters defined by a data-driven cluster analysis. We use maximum likelihood methods to estimate parameters from our spatial autoregressive model. Our model protects against type-I errors, enables the detection of both localized and regional activations (including volume of interest effects), provides information on functional connectivity in the brain, and establishes a framework to produce spatially smoothed maps of distributed brain activity for each individual. We illustrate the application of our model using PET data from a study of working memory in individuals with schizophrenia.},
author = {Bowman, F Dubois and Dubois},
doi = {10.1093/biostatistics/kxi027},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Bowman, Dubois - 2005 - Spatio-temporal modeling of localized brain activity.pdf:pdf},
issn = {1465-4644},
journal = {Biostatistics},
keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Cluster Analysis,Memory,Memory: physiology,Models,Neurological,Positron-Emission Tomography,Schizophrenia,Schizophrenia: pathology},
month = {oct},
number = {4},
pages = {558--75},
pmid = {15843592},
title = {{Spatio-temporal modeling of localized brain activity.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15843592},
volume = {6},
year = {2005}
}
@article{Chumbley2010,
abstract = {In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.},
author = {Chumbley, Justin R and Worsley, K and Flandin, G and Friston, Karl J},
doi = {10.1016/j.neuroimage.2009.10.090},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Chumbley et al. - 2010 - Topological FDR for neuroimaging.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Animals,Brain,Brain Mapping,Brain Mapping: methods,Brain: anatomy {\&} histology,Brain: physiology,Computer-Assisted,Computer-Assisted: methods,Electroencephalography,Electroencephalography: methods,FDR,Humans,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,RFT,Reproducibility of Results,Sensitivity and Specificity,Three-Dimensional,Three-Dimensional: methods,topological inference},
mendeley-tags = {FDR,RFT,topological inference},
month = {mar},
number = {4},
pages = {3057--64},
pmid = {19944173},
publisher = {Elsevier Inc.},
title = {{Topological FDR for neuroimaging.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19944173},
volume = {49},
year = {2010}
}
@article{Chumbley2009,
abstract = {In this note, we revisit earlier work on false discovery rate (FDR) and evaluate it in relation to topological inference in statistical parametric mapping. We note that controlling the false discovery rate of voxels is not equivalent to controlling the false discovery rate of activations. This is a problem that is unique to inference on images, in which the underlying signal is continuous (i.e., signal which does not have a compact support). In brief, inference based on conventional voxel-wise FDR procedures is not appropriate for inferences on the topological features of a statistical parametric map (SPM), such as peaks or regions of activation. We describe the nature of the problem, illustrate it with some examples and suggest a simple solution based on controlling the false discovery rate of connected excursion sets within an SPM, characterised by their volume.},
author = {Chumbley, Justin R and Friston, Karl J},
doi = {10.1016/j.neuroimage.2008.05.021},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Chumbley, Friston - 2009 - False discovery rate revisited FDR and topological inference using Gaussian random fields.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Artifacts,Brain,Brain Mapping,Brain Mapping: methods,Brain: radionuclide imaging,Computer-Assisted,Computer-Assisted: methods,Electroencephalography,FDR,Humans,Image Processing,Imaging,Normal Distribution,RFT,Three-Dimensional,topological inference},
mendeley-tags = {FDR,RFT,topological inference},
month = {jan},
number = {1},
pages = {62--70},
pmid = {18603449},
publisher = {Elsevier Inc.},
title = {{False discovery rate revisited: FDR and topological inference using Gaussian random fields.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18603449},
volume = {44},
year = {2009}
}
@article{Worsley1996,
abstract = {We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the P-value for local maxima of Gaussian, t, chi(2) and F fields over search regions of any shape or size in any number of dimensions. This unifies the P-values for large search areas in 2-D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699) large search regions in 3-D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900-918) and the usual uncorrected P-value at a single pixel or voxel.},
author = {Worsley, K J and Marrett, S and Neelin, P and Vandal, a C and Friston, Karl J and Evans, a C},
doi = {10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Worsley et al. - 1996 - A unified statistical approach for determining significant signals in images of cerebral activation.pdf:pdf},
issn = {1065-9471},
journal = {Human brain mapping},
keywords = {MTP,RFT,euler characteristic,fMRI,fmri,pet,random fields,topological inference},
mendeley-tags = {MTP,RFT,fMRI,topological inference},
month = {jan},
number = {1},
pages = {58--73},
pmid = {20408186},
title = {{A unified statistical approach for determining significant signals in images of cerebral activation.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20408186},
volume = {4},
year = {1996}
}
@article{Friston1991,
abstract = {Statistical parametric maps (SPMs) are potentially powerful ways of localizing differences in regional cerebral activity. This potential is limited by uncertainties in assessing the significance of these maps. In this report, we describe an approach that may partially resolve this issue. A distinction is made between using SPMs as images of change significance and using them to identify foci of significant change. In the first case, the SPM can be reported nonselectively as a single mathematical object with its omnibus significance. Alternatively, the SPM constitutes a large number of repeated measures over the brain. To reject the null hypothesis, that no change has occurred at a specific location, a threshold adjustment must be made that accounts for the large number of comparisons made. This adjustment is shown to depend on the SPM's smoothness. Smoothness can be determined empirically and be used to calculate a threshold required to identify significant foci. The approach models the SPM as a stationary stochastic process. The theory and applications are illustrated using uniform phantom images and data from a verbal fluency activation study of four normal subjects.},
author = {Friston, Karl J and Frith, C D and Liddle, P F and Frackowiak, R S},
doi = {10.1038/jcbfm.1991.122},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Friston et al. - 1991 - Comparing functional (PET) images the assessment of significant change.pdf:pdf},
issn = {0271-678X},
journal = {Journal of cerebral blood flow and metabolism},
keywords = {Adult,Cerebrovascular Circulation,Computer-Assisted,Emission-Computed,Humans,Image Processing,Male,RFT,Statistics as Topic,Stochastic Processes,Tomography,fMRI},
mendeley-tags = {RFT,fMRI},
month = {jul},
number = {4},
pages = {690--9},
pmid = {2050758},
title = {{Comparing functional (PET) images: the assessment of significant change.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/2050758},
volume = {11},
year = {1991}
}
@article{Worsley1992,
abstract = {Many studies of brain function with positron emission tomography (PET) involve the interpretation of a subtracted PET image, usually the difference between two images under baseline and stimulation conditions. The purpose of these studies is to see which areas of the brain are activated by the stimulation condition. In many cognitive studies, the activation is so slight that the experiment must be repeated on several subjects and the subtracted images are averaged to improve the signal-to-noise ratio. The averaged image is then standardized to have unit variance and then searched for local maxima. The main problem facing investigators is which of these local maxima are statistically significant. We describe a simple method for determining an approximate p value for the global maximum based on the theory of Gaussian random fields. The p value is proportional to the volume searched divided by the product of the full widths at half-maximum of the image reconstruction process or number of resolution elements. Rather than working with local maxima, our method focuses on the Euler characteristic of the set of voxels with a value larger than a given threshold. The Euler characteristic depends only on the topology of the regions of high activation, irrespective of their shape. For large threshold values this is approximately the same as the number of isolated regions of activation above the threshold. We can thus not only determine if any activation has taken place, but we can also estimate how many isolated regions of activation are present.},
author = {Worsley, K J and Evans, a C and Marrett, S and Neelin, P},
doi = {10.1038/jcbfm.1992.127},
file = {:Users/Joke/Library/Application Support/Mendeley Desktop/Downloaded/Worsley et al. - 1992 - A three-dimensional statistical analysis for CBF activation studies in human brain.pdf:pdf},
issn = {0271-678X},
journal = {Journal of cerebral blood flow and metabolism},
keywords = {Brain,Brain: blood supply,Brain: radionuclide imaging,Cerebrovascular Circulation,Cognition,Computer Simulation,Emission-Computed,Emission-Computed: methods,Humans,MTP,Pain,Pain: radionuclide imaging,RFT,Statistics as Topic,Tomography,fMRI,topological inference},
mendeley-tags = {MTP,RFT,fMRI,topological inference},
month = {nov},
number = {6},
pages = {900--18},
pmid = {1400644},
title = {{A three-dimensional statistical analysis for CBF activation studies in human brain.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/1400644},
volume = {12},
year = {1992}
}