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Functional Magnetic Resonance Images acquired during resting-state provideinformation about the functional organization of the brain through measuringcorrelations between brain areas. Independent components analysis is thereference approach to estimate spatial components from weakly structured datasuch as brain signal time courses; each of these components may be referred toas a brain network and the whole set of components can be conceptualized as abrain functional atlas. Recently, new methods using a sparsity prior haveemerged to deal with low signal-to-noise ratio data. However, even when usingsophisticated priors, the results may not be very sparse and most often do notseparate the spatial components into brain regions. This work presentspost-processing techniques that automatically sparsify brain maps and separateregions properly using geometric operations, and compares these techniquesaccording to faithfulness to data and stability metrics. In particular, amongthreshold-based approaches, hysteresis thresholding and random walkersegmentation, the latter improves significantly the stability of both dense andsparse models.

Region segmentation for sparse decompositions: better brain parcellations from rest fMRI