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