Complexity of intrinsic brain dynamics shaped by multiscale structural constraints

Abstract

AbstractThe brain is a complex, nonlinear system, exhibiting ever-evolving patterns of activities even without external inputs or tasks. Such intrinsic dynamics play a key role in cognitive functions and psychiatric disorders. A challenge is to link the intrinsic dynamics to the underlying structure, given the nonlinearity. Here we use a biophysically constrained, nonlinear-dynamical model to show how the complexity of intrinsic brain dynamics, manifested as its multistability and temporal diversity, can be sculpted by structural properties across scales. At a local level, multistability and temporal diversity can be induced by sufficient recurrent excitatory connectivity and its heterogeneity. At a global level, such functional complexity can also be created by the synergistic interaction between monostable, locally identical regions. Coordination between model brain regions across attractors in the multistable landscape predicts human functional connectivity. Compared to dynamics near a single attractor, cross-attractor coordination better accounts for functional links uncorrelated with structural connectivity. Energy costs of cross-attractor coordination are modulated by both local and global connectivity, and higher in the Default Mode Network. These findings hint that functional connectivity underscores transitions between alternative patterns of activity in the brain—even more than the patterns themselves. This work provides a systematic framework for characterizing intrinsic brain dynamics as a web of cross-attractor transitions and their energy costs. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.Significance StatementThe brain is a multifunctional system: different brain regions can coordinate flexibly to perform different tasks. Understanding the regional and global structural constraints on brain function is critical to understand cognition. Here, using a unified biophysical network model, we show how structural constraints across scales jointly shape the brain’s intrinsic functional repertoire – the set of all possible patterns that a brain could generate. The modeled functional repertoire is enriched by both the flexibility of individual brain regions and their synergistic interaction in a global network. By carefully examining the modeled repertoire, we found that human resting-state functional connectivity was better predicted by transitions between brain activity patterns rather than any specific pattern per se.