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

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. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the DMN. Moreover, we computed, using Susceptibility Propagation (SP), the matrix of correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting States Networks determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. We then show preliminary results indicating our predictions on how functional DMN maps change when the anatomical brain network suffers structural anomalies, like in Alzheimer's Disease and in lesions of the Corpus Callosum.

Methods for decoding cortical gradients of functional connectivity

“…S12 ). Clustering similarity was determined using the normalized mutual information (NMI) (Peraza et al, 2020).…”

The 2-D Cluster Variation Method: Initial Findings and Topography Illustrations

“…More recently, Parr et al discuss neuronal message passing [39], with a good review of relevant literature. Recent work on neural connectivity in the brain by Peraza-Goicolea et al (2020) addresses susceptibility propagation (SP), belief propagation (BP), and critical state dynamics [40], and Friston, Parr, and de Vries (2017) investigate the connection between belief propagation and active inference [16]. Thus, the substantial work on message passing in neural systems may usefully inform further investigations on using a 2-D CVM as a means for modeling neural systems.…”

Modeling Neurodegeneration in silico With Deep Learning

“…Prior work in modeling neurological diseases have primarily explored computational models of cellular processes focusing on pathological changes such as excitotoxicity or abnormal bioenergetics (Le Masson et al, 2014;Muddapu et al, 2019), or connectome models based on structural or functional connectivity in the brain (Hof et al, 1997;Raj et al, 2012;Zhou et al, 2012;Ortiz et al, 2015;Peraza-Goicolea et al, 2020;Vanasse et al, 2021). Early work investigating structural or functional connectomes primarily focused on modeling specific aspects of disease progression, such as diffusive spread of misfolded tau and beta amyloids (Raj et al, 2012) or changes in network connectivity contributing to disease vulnerability or diagnosis (Zhou et al, 2012;Ortiz et al, 2015).…”

“…Early work investigating structural or functional connectomes primarily focused on modeling specific aspects of disease progression, such as diffusive spread of misfolded tau and beta amyloids (Raj et al, 2012) or changes in network connectivity contributing to disease vulnerability or diagnosis (Zhou et al, 2012;Ortiz et al, 2015). More recent work has begun using the connectome to simulate disease states in silico, for example by modifying connection weights in a simulated functional connectome to predict changes in functional activation and connectivity in the brain (Peraza-Goicolea et al, 2020). A meta-analysis of the relationship between structural pathology and behavioral pathology supported the notion that network degeneration is a contributing factor to disease pathology (Vanasse et al, 2021).…”