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Understanding the relationship between the structure and function of thehuman brain is one of the most important open questions in Neurosciences. Inparticular, Resting State Networks (RSN) and more specifically the Default ModeNetwork (DMN) of the brain, which are defined from the analysis of functionaldata lack a definitive justification consistent with the anatomical structureof the brain. In this work, we show that a possible connection may naturallyrest on the idea that information flows in the brain through a neuralmessage-passing dynamics between macroscopic structures, like those defined bythe human connectome (HC). In our model, each brain region in the HC is assumedto have a binary behavior (active or not), the strength of interactions amongthem is encoded in the anatomical connectivity matrix defined by the HC, andthe dynamics of the system is defined by a neural message-passing algorithm,Belief Propagation (BP), working near the critical point of the humanconnectome. We show that in the absence of direct external stimuli the BPalgorithm converges to a spatial map of activations that is similar to the DMN.Moreover, we computed, using Susceptibility Propagation (SP), the matrix ofcorrelations between the different regions and show that the modules defined bya clustering of this matrix resemble several Resting States Networks determinedexperimentally. Both results suggest that the functional DMN and RSNs can beseen as simple consequences of the anatomical structure of the brain and aneural message-passing dynamics between macroscopic regions. We then showpreliminary results indicating our predictions on how functional DMN mapschange when the anatomical brain network suffers structural anomalies, like inAlzheimers Disease and in lesions of the Corpus Callosum.

Modeling functional resting-state brain networks through neural message passing on the human connectome