The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure

Abstract

A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of the brain. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states – characterized by minimal energy – display common activation pro les across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-activated frequently. The predicted activation rate of these systems is highly correlated with the observed activation rate measured in a separate resting state fMRI data set, validating the utility of the maximum entropy model in describing neurophysiological dynamics. This approach also offers a formal notion of the energy of activity within a system, and the energy of activity shared between systems. We observe that within-and between-system energies cleanly separate cognitive systems into distinct categories, optimized for di erential contributions to integrated versus segregated function. These results support the notion that energetic and structural constraints circumscribe brain dynamics, o ering insights into the roles that cognitive systems play in driving whole-brain activation patterns.