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MVPA tutorial - Rogers lab brain imaging unit

Pattern classification problems in the context of neuroimaging data are often highly underdetermined. For example, a typical fMRI data might have 100,000 features (voxels) with only a few hunderds of training examples (stimuli presented). To tackle this issue of underdeterminacy while fitting whole brain models (i.e. without pre-defining ROI), we tend to use sparse methods, such as the Logistic LASSO, which will be the main focus of this tutorial.

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Picture from: The Elements of Statistical Learning