Year: 2017
Type: article
Abstract: The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.
Source: NeuroImage
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,540
Funders: National Science Foundation, John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, National Institutes of Health, Office of Naval Research, National Institute of Mental Health, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Army Research Office, Army Research Laboratory
Awards: G1001159199, G1024130705, G1041393086, G1099660080, G1177916794, G1778482337, G1993459732, G2066955304, G2130352337, G2236980202, Virtual Resection to Treat Epilepsy, G2252567680, G235261508, G2425642435, G2864901048, G2887601406, G2901702611, G3137615481, G3330175468, G3424441068, G3564833335, G3611952402, G3690479056, G3755125221, G3824545241, G4069802742, G4503518981, Track-2: The Smart MATerial Design, Analysis, and Processing (SMATDAP) consortium: Building next-generation polymers and the tools to accelerate cost-effective commercial productio, G4652607670, G5172716836, G5253651465, G5259331294, G572628577, G5884259427, CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics, G6671297155, G6689184838, G6752185412, Linguistic and Nonlinguistic Functions of Frontal Cortex, G6981911394, Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence, G7159985717, G7452299184, WORKSHOP: Quantitative Theories of Learning, Memory, and Prediction, G7530591242, G7594130654, G7747382488, G793584043, G7989609580, G8454816522, CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, G8554419822, G90128336, G91481811, G916315028, G91658921
OpenAlex ID: W2963186114