2011/04/22 by Vincent Michel, Alexandre Gramfort, Gaël Varoquaux +3
Computer Science · Neuroscience · #Artificial intelligence #Blind Source Separation Techniques #Cluster analysis #Computer science #Curse of dimensionality #Data mining #Dimensionality reduction #Face and Expression Recognition #Feature (linguistics) #Feature selection #Functional Brain Connectivity Studies #Hierarchical clustering #Machine learning #Multivariate statistics #Pattern recognition (psychology) #Univariate #Voxel #cs.CV
paper · pdf · doi:10.1016/j.patcog.2011.04.006
published as Pattern Recognition (2011)
openalex publication_date 2011/04/22 · arxiv created 2011/04/28 · arxiv updated 2011/04/29 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/06
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.