A supervised clustering approach for fMRI-based inference of brain states

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.
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Pattern Recognition, Elsevier, 2011, epub ahead of print. 〈10.1016/j.patcog.2011.04.006〉
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Vincent Michel, Alexandre Gramfort, Gaël Varoquaux, Evelyn Eger, Christine Keribin, et al.. A supervised clustering approach for fMRI-based inference of brain states. Pattern Recognition, Elsevier, 2011, epub ahead of print. 〈10.1016/j.patcog.2011.04.006〉. 〈inria-00589201〉

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