Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control
Year: 2018
Type: preprint
Abstract: 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 deploy an unsupervised machine learning algorithm to define brain states as time point level activity patterns from functional magnetic resonance imaging data acquired during 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 brain states. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to support 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 memory performance. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.
DOI: 10.1101/412429
Institution: University of Pennsylvania
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funders: National Science Foundation, John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, Institute for Scientific Interchange, Office of Naval Research, National Institute of Mental Health, National Institute of Child Health and Human Development, Army Research Office, Army Research Laboratory
Awards: G1001159199, G1024130705, G1041393086, G1099660080, G1177916794, G129050353, G129985460, G1318417280, G1350070852, G1483473580, G157307749, G1630141903, G1778482337, G1993459732, G2066955304, G2130352337, G2236980202, G2244810882, G2247336289, G2252567680, G235261508, G2425642435, G2481140831, G2519463293, G2668337057, G2864901048, G2887601406, G2901702611, G2942273589, G3137615481, G3141641755, G3330175468, G334592757, G3424441068, G3437992771, G3564833335, G3574985532, G3611952402, G3690479056, G3755125221, G3933175036, G4069802742, G4526399220, G470506863, G4823423002, G5172716836, G5253651465, G5259331294, G5419322094, G5469137442, G5496178531, G5760707814, NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control, G5884259427, CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, G6467593695, G656215873, G657448715, CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics, G6689184838, G6752185412, G6981911394, G7159985717, G7236036932, G7452299184, WORKSHOP: Quantitative Theories of Learning, Memory, and Prediction, G7530591242, G7538535954, G7594130654, G7747382488, G7838535007, G7881329, G793584043, G7989609580, G8164210364, G8454816522, G8554419822, G8796586358, G8895310522, G91481811, G916315028, G91658921, G970350945
OpenAlex ID: W4289549037