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Spontaneous brain activity, as observed in functional neuroimaging, has beenshown to display reproducible structure that expresses brain architecture andcarries markers of brain pathologies. An important view of modern neuroscienceis that such large-scale structure of coherent activity reflects modularityproperties of brain connectivity graphs. However, to date, there has been nodemonstration that the limited and noisy data available in spontaneous activityobservations could be used to learn full-brain probabilistic models thatgeneralize to new data. Learning such models entails two main challenges: i)modeling full brain connectivity is a difficult estimation problem that facesthe curse of dimensionality and ii) variability between subjects, coupled withthe variability of functional signals between experimental runs, makes the useof multiple datasets challenging. We describe subject-level brain functionalconnectivity structure as a multivariate Gaussian process and introduce a newstrategy to estimate it from group data, by imposing a common structure on thegraphical model in the population. We show that individual models learned fromfunctional Magnetic Resonance Imaging (fMRI) data using this population priorgeneralize better to unseen data than models based on alternativeregularization schemes. To our knowledge, this is the first report of across-validated model of spontaneous brain activity. Finally, we use theestimated graphical model to explore the large-scale characteristics offunctional architecture and show for the first time that known cognitivenetworks appear as the integrated communities of functional connectivity graph.

Brain covariance selection: better individual functional connectivity models using population prior