1. A supervised clustering approach for fMRI-based inference of brain states
- Author
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Michel, Vincent, Gramfort, Alexandre, Varoquaux, Gaël, Eger, Evelyn, Keribin, Christine, and Thirion, Bertrand
- Subjects
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MAGNETIC resonance imaging of the brain , *BRAIN physiology , *CLUSTER analysis (Statistics) , *FEATURE extraction , *DIMENSION reduction (Statistics) , *MULTIVARIATE analysis - Abstract
Abstract: We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject''s behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task. [Copyright &y& Elsevier]
- Published
- 2012
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