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Optimal trajectories of brain state transitions.
- Source :
-
NeuroImage [Neuroimage] 2017 Mar 01; Vol. 148, pp. 305-317. Date of Electronic Publication: 2017 Jan 11. - Publication Year :
- 2017
-
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.<br /> (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)
- Subjects :
- Adult
Algorithms
Attention physiology
Brain diagnostic imaging
Brain pathology
Brain Injuries, Traumatic diagnostic imaging
Brain Injuries, Traumatic pathology
Brain Injuries, Traumatic psychology
Cerebral Cortex diagnostic imaging
Cerebral Cortex pathology
Cerebral Cortex physiology
Cognition physiology
Cognition Disorders diagnostic imaging
Cognition Disorders parasitology
Cognition Disorders psychology
Diffusion Magnetic Resonance Imaging
Energy Metabolism physiology
Executive Function physiology
Female
Healthy Volunteers
Humans
Male
Psychomotor Performance physiology
White Matter anatomy & histology
White Matter diagnostic imaging
White Matter pathology
Young Adult
Brain anatomy & histology
Subjects
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 148
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 28088484
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2017.01.003