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PLoS Comput Biol. 2015 Jan 8;11(1):e1004029. doi: 10.1371/journal.pcbi.1004029. eCollection 2015 Jan.

Brain network adaptability across task states.

Author information

1
Department of Mechanical & Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America; Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America.
2
Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America.
3
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
4
University of Oxford Medical Sciences Division, John Radcliffe Hospital, Headington, Oxford, United Kingdom; Behavioural and Clinical Neuroscience Institute and Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
5
Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.

Abstract

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.

PMID:
25569227
PMCID:
PMC4287347
DOI:
10.1371/journal.pcbi.1004029
[Indexed for MEDLINE]
Free PMC Article

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