Optimal Trajectories of Brain State Transitions

TL;DR

This paper models how brain anatomy constrains neural state transitions using network control theory, identifying key control regions and examining effects of injury on control mechanisms.

Contribution

It introduces a computational framework linking white matter architecture to brain state transitions and highlights specific control hubs and their vulnerability post-injury.

Findings

Supramarginal gyrus and inferior parietal lobule are efficient control hubs.

Control regions are involved in diverse state transitions beyond traditional control notions.

Traumatic brain injury reduces control specificity, increasing susceptibility to noise.

Abstract

The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how the organization of white matter architecture constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question from a computational perspective by defining a brain state as a pattern of activity across brain regions. Drawing on recent advances in network control theory, we model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. Specifically, we examine how the brain moves from a specified initial state (characterized by high activity in the default mode) to a specified target state (characterized by high activity in primary sensorimotor cortex) in finite time. Across all state transitions, we observe that the supramarginal gyrus and the inferior parietal lobule consistently acted as efficient, low energy control hubs, consistent with their strong anatomical connections to key input areas of sensorimotor cortex. Importantly, both these and other regions in the fronto-parietal, cingulo-opercular, and attention systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional notions of control common in the engineering literature. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to damage-induced noise in neurophysiological activity. These results offer fundamentally new insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.