1 Modeling functional resting-state brain networks through neural message passing on the human connectome Julio A. Peraza-Goicoleaa, Eduardo Martínez-Montesb,c*, Eduardo Aubertb, Pedro A. Valdés-Hernándezd, and Roberto Muleta a Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Havana, Cuba b Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba c Advanced Center for Electrical and Electronic Engineering (AC3E), Universidad Técnica Federico Santa María, Valparaíso, Chile d Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, USA *Corresponding author: Calle 190 #15202 entre Ave 25 y 27, Cubanacan, Playa, Habana 11600 Phone: +53 7 263 7252 E-mail:
[email protected] (E. Martínez-Montes) 2 Abstract Understanding the relationship between the structure and function of the human brain is one of the most important open questions in Neurosciences. In particular, Resting State Networks (RSN) and more specifically the Default Mode Network (DMN) of the brain, which are defined from the analysis of functional data lack a definitive justification consistent with the anatomical structure of the brain. In this work we show that a possible connection may naturally rest on the idea that information flows in the brain through a neural message-passing dynamics between macroscopic structures, like those defined by the human connectome (HC). In our model, each brain region in the HC is assumed to have a binary behavior (active or not), the strength of interactions among them is encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by a neural message-passing algorithm, Belief Propagation (BP), working near the critical point of the human connectome.