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

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. Dimensi...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Vincent Michel, Alexandre Gramfort, Gaël Varoquaux, Evelyn Eger, Christine Keribin, Bertrand Thirion
• Keywords: Brain Regions, CORR 2011, Dimensionality Reduction, Education, Functional Magnetic Resonance Imaging