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 cogni- tive states. Here, we deploy an unsupervised machine learning algorithm to define brainstates as time point level activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task. We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics . Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brainstates. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to sup- port states of high activity in default mode areas, while requiring relatively low energy . Finally, we show that brain state dynamics change throughout development and explain working mem- ory performance . Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.
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.
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cogni- tive
cognitive
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deploy an unsupervised machine learning algorithm to define brainstates as time point level
analyze the brain's trajectories through a high-dimensional activity space at the level of single time point
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dur- ing
during
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brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics
specific state space trajectories, which represent temporal sequences of brain activity, are modulated by cognitive load and related to task performance
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show that structural connectivity constrains the temporal progression of brainstates. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to sup- port states of high activity in default mode areas, while requiring relatively low energy
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
R
show that
examine associations between age and time-resolved
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change throughout development and explain working mem- ory performance
, revealing new insights into functional changes in the default mode and executive control networks