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TAPAS PhysIO Toolbox

Current version: Release 2022a, v8.1.0

Copyright (C) 2012-2022
Lars Kasper
kasper@biomed.ee.ethz.ch

Translational Neuromodeling Unit (TNU)
Institute for Biomedical Engineering
University of Zurich and ETH Zurich

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Purpose

The general purpose of this Matlab toolbox is the model-based physiological noise correction of fMRI data using peripheral measures of respiration and cardiac pulsation. It incorporates noise models of cardiac/respiratory phase (RETROICOR, Glover et al. 2000), as well as heart rate variability and respiratory volume per time (cardiac response function, Chang et. al, 2009, respiratory response function, Birn et al. 2006), and extended motion models. While the toolbox is particularly well integrated with SPM via the Batch Editor GUI, its simple output nuisance regressor text files can be incorporated into any major neuroimaging analysis package.

Core design goals for the toolbox were: flexibility, robustness, and quality assurance to enable physiological noise correction for large-scale and multi-center studies.

Some highlights:

  1. Robust automatic preprocessing of peripheral recordings via iterative peak detection, validated in noisy data and patients.
  2. Flexible support of peripheral data formats (Siemens, Philips, HCP, GE, Biopac, ...) and noise models (RETROICOR, RVHRCOR).
  3. Fully automated noise correction and performance assessment for group studies.
  4. Integration in fMRI pre-processing pipelines as SPM Toolbox (Batch Editor GUI).

The accompanying technical paper about the toolbox concept and methodology can be found at: https://doi.org/10.1016/j.jneumeth.2016.10.019

Installation

Matlab

  1. Unzip the TAPAS archive in your folder of choice
  2. Open Matlab
  3. Go to /your/path/to/tapas/physio/code
  4. Run tapas_physio_init() in Matlab

Note: Step (4) executes the following steps, which you could do manually as well.

  • Adds the physio/code/ folder to your Matlab path
  • Adds SPM to your Matlab path (you can enter it manually, if not found)
  • Links the folder (Linux/Max) or copies the folder (Windows) physio/code/ to /your/path/to/SPM/toolbox/PhysIO, if the PhysIO code is not already found there

Only the first point is necessary for using PhysIO standalone with Matlab. The other two points enable PhysIO's SPM integration, i.e., certain functionality (Batch Editor GUI, pipeline dependencies, model assessment via F-contrasts).

Getting Started

...following the installation, you can try out an example:

  1. Download the TAPAS examples via running tapas_download_example_data() (found in misc-subfolder of TAPAS)
    • The PhysIO Example files will be downloaded to tapas/examples/<tapas-version>/PhysIO
  2. Run philips_ecg3t_matlab_script.m in subdirectory Philips/ECG3T
  3. See subdirectory physio/docs and the next two section of this document for help.

You may try any of the examples in the other vendor folders as well.

Contact/Support

We are very happy to provide support on how to use the PhysIO Toolbox. However, as every researcher, we only have a limited amount of time. So please excuse, if we might not provide a detailed answer to your request, but just some general pointers and templates. Before you contact us, please try the following:

  1. A first look at the FAQ (which is frequently extended) might already answer your questions.
  2. A lot of questions (before 2018) have also been discussed on our mailinglist tapas@sympa.ethz.ch, which has a searchable archive.
  3. For new requests, we would like to ask you to submit them as issues on our github release page for TAPAS, which is also an up-to-date resource to user-driven questions (since 2018).

Documentation

Documentation for this toolbox is provided in the following forms

  1. Overview and guide to further documentation: README.md and CHANGELOG.md
    • README.md: this file, purpose, installation, getting started, pointer to more help
    • CHANGELOG.md: List of all toolbox versions and the respective release notes, i.e. major changes in functionality, bugfixes etc.
  2. User Guide: The markdown-based GitLab Wiki, including an FAQ
    • online (and frequently updated) at http://gitlab.ethz.ch/physio/physio-doc/wikis/home.
    • offline (with stables releases) as part of the toolbox in folder physio/wikidocs:
      • plain text .md markdown files
      • as single HTML and PDF file: documentation.{html,pdf}
  3. Within SPM: All toolbox parameters and their settings are explained in the Help Window of the SPM Batch Editor
  4. Within Matlab: Extensive header at the start of each tapas_physio_* function and commenting
    • accessible via help and doc commands from Matlab command line
    • starting point for all parameters (comments within file): edit tapas_physio_new
    • also useful for developers (technical documentation)

Background

The PhysIO Toolbox provides physiological noise correction for fMRI-data from peripheral measures (ECG/pulse oximetry, breathing belt). It is model-based, i.e. creates nuisance regressors from the physiological monitoring that can enter a General Linear Model (GLM) analysis, e.g. SPM8/12. Furthermore, for scanner vendor logfiles (PHILIPS, GE, Siemens), it provides means to statistically assess peripheral data (e.g. heart rate variability) and recover imperfect measures (e.g. distorted R-peaks of the ECG).

Facts about physiological noise in fMRI:

  • Physiological noise can explain 20-60 % of variance in fMRI voxel time series (Birn2006, Hutton2011, Harvey2008)
    • Physiological noise affects a lot of brain regions (s. figure, e.g. brainstem or OFC), especially next to CSF, arteries (Hutton2011).
    • If not accounted for, this is a key factor limiting sensitivity for effects of interest.
  • Physiological noise contributions increase with field strength; they become a particular concern at and above 3 Tesla (Kasper2009, Hutton2011).
  • In resting state fMRI, disregarding physiological noise leads to wrong connectivity results (Birn2006).
  • Uncorrected physiological noise introduces serial correlations into the residual voxel time series, that invalidate assumptions on noise correlations (e.g., AR(1)) used in data prewhitening by all major analysis packages. This issue is particularly aggravated at short TR (<1s), and most of its effects can be suitably addressed by physiological noise correction (Bollmann2018)

Therefore, some kind of physiological noise correction is highly recommended for every statistical fMRI analysis.

Model-based correction of physiological noise:

  • Physiological noise can be decomposed into periodic time series following heart rate and breathing cycle.
  • The Fourier expansion of cardiac and respiratory phases was introduced as RETROICOR (RETROspective Image CORrection, Glover2000, see also Josephs1997).
  • These Fourier Terms can enter a General Linear Model (GLM) as nuisance regressors, analogous to movement parameters.
  • As the physiological noise regressors augment the GLM and explain variance in the time series, they increase sensitivity in all contrasts of interest.

Features of this Toolbox

Physiological Noise Modeling

  • Modeling physiological noise regressors from peripheral data (breathing belt, ECG, pulse oximeter)
    • State of the art RETROICOR cardiac and respiratory phase expansion
    • Cardiac response function (Chang et al, 2009) and respiratory response function (Birn et al. 2006) modelling of heart-rate variability and respiratory volume per time influence on physiological noise
    • Flexible expansion orders to model different contributions of cardiac, respiratory and interaction terms (see Harvey2008, Hutton2011)
  • Data-driven noise regressors
    • PCA extraction from nuisance ROIs (CSF, white matter), similar to aCompCor (Behzadi2007)

Automatization and Performance Assessment

  • Automatic creation of nuisance regressors, full integration into standard GLMs, tested for SPM8/12 (multiple_regressors.mat)
  • Integration in SPM Batch Editor: GUI for parameter input, dependencies to integrate physiological noise correction in preprocessing pipeline
  • Performance Assessment: Automatic F-contrast and tSNR Map creation and display for groups of physiological noise regressors, using SPM GLM tools via tapas_physio_report_contrasts().

Flexible Read-in

The toolbox is dedicated to seamless integration into a clinical research setting and therefore offers correction methods to recover physiological data from imperfect peripheral measures. Read-in of the following formats is currently supported (alphabetic order):

  • Biopac .mat and .txt -export
  • Brain Imaging Data Structure (BIDS for *_physio.tsv[.gz]/.json files)
  • Custom logfiles: should contain one amplitude value per line, one logfile per device. Sampling interval(s) are provided as a separate parameter to the toolbox.
  • General Electric
  • Philips SCANPHYSLOG files (all versions from release 2.6 to 5.3)
  • Siemens VB (files .ecg, .resp, .puls)
  • Siemens VD (files *_ECG.log, *_RESP.log, *_PULS.log)
  • Siemens Human Connectome Project (preprocessed files *Physio_log.txt)

See also the Wiki page on Read-In for a more detailed list and description of the supported formats.

Compatibility

  • Matlab Toolbox
  • Input:
    • Fully integrated to work with physiological logfiles for Philips MR systems (SCANPHYSLOG)
    • tested for General Electric (GE) log-files
    • implementation for Siemens log-files (both VB and VD/VE, CMRR multiband)
    • also: interface for 'Custom', i.e. general heart-beat time stamps & breathing volume time courses from other log formats
    • BioPac
    • ... (other upcoming formats)
  • Output:
    • Nuisance regressors for mass-univariate statistical analysis with SPM5,8,12 or as text file for export to any other package
    • raw and processed physiological logfile data
    • Graphical Batch Editor interface to SPM
  • Part of the TAPAS Software Collection of the Translational Neuromodeling Unit (TNU) Zurich
    • ensures long term support and ongoing development

Contributors

  • Lead Programmer:
    • Lars Kasper, TNU & MR-Technology Group, IBT, University of Zurich & ETH Zurich
  • Project Team:
    • Steffen Bollmann, Centre for Advanced Imaging, University of Queensland, Australia
    • Saskia Bollmann, Centre for Advanced Imaging, University of Queensland, Australia
    • Sam Harrison, TNU Zurich
  • Contributors (Code):
    • Eduardo Aponte, TNU Zurich
    • Tobias U. Hauser, FIL London, UK
    • Jakob Heinzle, TNU Zurich
    • Chloe Hutton, FIL London, UK (previously)
    • Miriam Sebold, Charite Berlin, Germany
    • External TAPAS contributors are listed in the Contributor License Agreement
  • Contributors (Examples):

Requirements

  • All specific software requirements and their versions are in a separate file in this folder, requirements.txt.
  • In brief:
    • PhysIO needs Matlab to run, and a few of its toolboxes.
    • Some functionality requires SPM (GUI, nuisance regression, contrast reporting, writing residual and SNR images).

Acknowledgements

The PhysIO Toolbox ships with the following publicly available code from other open source projects and gratefully acknowledges their use.

  • utils\tapas_physio_propval.m
    • propval function from Princeton MVPA toolbox (GPL) a nice wrapper function to create flexible propertyName/value optional parameters
  • utils\tapas_physio_fieldnamesr.m

Cite Me

Main Toolbox and TAPAS Reference

Please cite the following papers in all of your publications that utilized the PhysIO Toolbox.

  1. Kasper, L., Bollmann, S., Diaconescu, A.O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T.U., Sebold, M., Manjaly, Z.-M., Pruessmann, K.P., Stephan, K.E., 2017. The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data. Journal of Neuroscience Methods 276, 56-72. https://doi.org/10.1016/j.jneumeth.2016.10.019
    • main PhysIO Toolbox reference
  2. Frässle, S., Aponte, E.A., Bollmann, S., Brodersen, K.H., Do, C.T., Harrison, O.K., Harrison, S.J., Heinzle, J., Iglesias, S., Kasper, L., Lomakina, E.I., Mathys, C., Müller-Schrader, M., Pereira, I., Petzschner, F.H., Raman, S., Schöbi, D., Toussaint, B., Weber, L.A., Yao, Y., Stephan, K.E., 2021. TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry. Frontiers in Psychiatry 12, 857. https://doi.org/10.3389/fpsyt.2021.680811
    • main TAPAS software collection reference

You can include the following snippet in your Methods section, along with a brief description of the physiological noise models used:

The analysis was performed using the Matlab PhysIO Toolbox ([1], version x.y.z, open-source code available as part of the TAPAS software collection: [2], https://www.translationalneuromodeling.org/tapas)

If you use respiratory volume per time (RVT) regressors or preprocess respiratory traces for RETROICOR, please also cite:

  1. Harrison, S.J., Bianchi, S., Heinzle, J., Stephan, K.E., Iglesias, S., Kasper L., 2021. A Hilbert-based method for processing respiratory timeseries. NeuroImage, 117787. https://doi.org/10.1016/j.neuroimage.2021.117787
    • superior RVT computation, preprocessing of respiratory traces

Our FAQ contains a suggestion for a more comprehensive RETROICOR-related methods paragraph. See the main TAPAS README for more details on citing TAPAS itself.

Related Papers (Implemented noise correction algorithms and optimal parameter choices)

The following sections list papers that

  • first implemented specific noise correction algorithms
  • determined optimal parameter choices for these algorithms, depending on the targeted application
  • demonstrate the impact of physiological noise and the importance of its correction

It is loosely ordered by the dominant physiological noise model used in the paper. The list is by no means complete, and we are happy to add any relevant papers suggested to us.

RETROICOR

  1. Glover, G.H., Li, T.Q. & Ress, D. Image-based method for retrospective correction of Physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44, 162-7 (2000).

  2. Hutton, C. et al. The impact of Physiological noise correction on fMRI at 7 T. NeuroImage 57, 101-112 (2011).

  3. Harvey, A.K. et al. Brainstem functional magnetic resonance imaging: Disentangling signal from PhysIOlogical noise. Journal of Magnetic Resonance Imaging 28, 1337-1344 (2008).

  4. Bollmann, S., Puckett, A.M., Cunnington, R., Barth, M., 2018. Serial correlations in single-subject fMRI with sub-second TR. NeuroImage 166, 152-166. https://doi.org/10.1016/j.neuroimage.2017.10.043

aCompCor / Noise ROIs

  1. Behzadi, Y., Restom, K., Liau, J., Liu, T.T., 2007. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90-101. https://doi.org/10.1016/j.neuroimage.2007.04.042

RVT

  1. Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008. The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage 40, 644-654. doi:10.1016/j.neuroimage.2007.11.059
  2. Jo, H.J., Saad, Z.S., Simmons, W.K., Milbury, L.A., Cox, R.W., 2010. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. NeuroImage 52, 571-582. https://doi.org/10.1016/j.neuroimage.2010.04.246
    • regressor delay suggestions

HRV

  1. Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage 44, 857-869. doi:10.1016/j.neuroimage.2008.09.029
  2. Shmueli, K., van Gelderen, P., de Zwart, J.A., Horovitz, S.G., Fukunaga, M., Jansma, J.M., Duyn, J.H., 2007. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. NeuroImage 38, 306-320. https://doi.org/10.1016/j.neuroimage.2007.07.037
    • regressor delay suggestions

Motion (Censoring, Framewise Displacement)

  1. Siegel, J.S., Power, J.D., Dubis, J.W., Vogel, A.C., Church, J.A., Schlaggar, B.L., Petersen, S.E., 2014. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Hum. Brain Mapp. 35, 1981-1996. https://doi.org/10.1002/hbm.22307

  2. Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142-2154. https://doi.org/10.1016/j.neuroimage.2011.10.018

Copying/License

The PhysIO Toolbox is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program (see the file LICENSE). If not, see http://www.gnu.org/licenses/.