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A diverse white matter network and finely tuned neuronal membrane propertiesallow the brain to transition seamlessly between cognitive states. However, itremains unclear how static structural connections guide the temporalprogression of large-scale brain activity patterns in different cognitivestates. Here, we analyze the brain's trajectories through a high-dimensionalactivity space at the level of single time point activity patterns fromfunctional magnetic resonance imaging data acquired during passive visualfixation (rest) and an n-back working memory task. We find that specific statespace trajectories, which represent temporal sequences of brain activity, aremodulated by cognitive load and related to task performance. Usingdiffusion-weighted imaging acquired from the same subjects, we use tools fromnetwork control theory to show that linear spread of activity along whitematter connections constrains the brain's state space trajectories at rest.Additionally, accounting for stimulus-driven visual inputs explains thedifferent trajectories taken during the n-back task. We also used models ofnetwork rewiring to show that these findings are the result of non-trivialgeometric and topological properties of white matter architecture. Finally, weexamine associations between age and time-resolved brain state dynamics,revealing new insights into functional changes in the default mode andexecutive control networks. Overall, these results elucidate the structuralunderpinnings of cognitively and developmentally relevant spatiotemporal braindynamics.

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands