Brain network adaptability across task states

Elizabeth N Davison; Kimberly J Schlesinger; Danielle S Bassett; Mary-Ellen Lynall; Michael B Miller; Scott T Grafton; Jean M Carlson

doi:10.1371/journal.pcbi.1004029

Brain network adaptability across task states

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

Authors

Elizabeth N Davison¹, Kimberly J Schlesinger², Danielle S Bassett³, Mary-Ellen Lynall⁴, Michael B Miller⁵, Scott T Grafton⁵, Jean M Carlson²

Affiliations

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adult
Brain / anatomy & histology*
Brain / physiology*
Brain Mapping / methods*
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging
Task Performance and Analysis

Grants and funding

This work was supported by the David and Lucile Packard Foundation and the Institute for Collaborative Biotechnologies through grant W911NF-09-0001 from the U.S. Army Research Office. KJS was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1144085. END and KJS were additionally supported by the Worster Fellowship. DSB was supported by the Alfred P. Sloan Foundation, the Institute for Translational Medicine and Therapeutics at Penn, and the Army Research Laboratory through contract number W911NF-10-2-0022. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.