MVPA tutorial - Rogers lab brain imaging unit [Wiki page]
I am organizing the brain imaging unit at the Knowledge and Concepts Lab, directed by Professor Tim Rogers. This is a tutorial (in progress) that introduces people to MVPA methods.
Neuroimaging data analysis is usually underdetermined. For example, a typical fMRI data might has 100,000 features (voxels) with only a few hunderds of training examples (stimuli presented). To tackle this issue of underdeterminacy while fitting the whole brain model (i.e. without pre-defining ROIs), we tend to use sparse methods, such as the Logistic LASSO, which will be the main focus of this tutorial.
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You can access the workshop schedule from the wiki page.
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Where and when: Wednesday 4pm at Psych 634
- Compare L1 & L2 penalty: Linear Regression
- Logistic Regression
- Compare L1 & L2 penalty: Logistic Regression
- Iterative Lasso
- Group Lasso, SOS Lasso
- Readings
- Matlab
- Glmnet: A Matlab toolbox for fitting the elastic-net for linear regression, logistic and multinomial regression, Poisson regression and the Cox model.
- Glmnet for Matlab (2013) Qian, J., Hastie, T., Friedman, J., Tibshirani, R. and Simon, N. http://www.stanford.edu/~hastie/glmnet_matlab/