Abstract
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.
Highlights
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour
The brain is a networked dynamical system that moves between diverse cognitive states to enable complex behaviours
We use network control theory to offer a mechanistic explanation for how the brain moves between cognitive states on the basis of white matter microstructure
Summary
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function. Neural systems alter their dynamics to meet task demands, enabling humans to perform the myriad complex cognitive functions necessary for everyday living. We hypothesize that the brain is theoretically controllable in the sense defined mathematically with the network control theory (see Methods) Since many such dynamic processes have an impact on distributed neural circuits rather than single brain regions alone, we conjecture that the brain is difficult to control via localized interventions. Which areas of the brain are most influential in driving changes in brain state trajectories? We aim to directly test for a relationship between conceptual notions of cognitive control and the mathematical notions of network control in the context of known cognitive systems[11]
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