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Subgraphs of functional brain networks identify dynamical constraints of cognitive control

Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions. To maneuver across an ever-changing landscape of mental states, the brain invokes cognitive control—a set of dynamic processes that engage and disengage different groups of brain regions to...

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Autores principales: Khambhati, Ankit N., Medaglia, John D., Karuza, Elisabeth A., Thompson-Schill, Sharon L., Bassett, Danielle S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056061/
https://www.ncbi.nlm.nih.gov/pubmed/29979673
http://dx.doi.org/10.1371/journal.pcbi.1006234
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author Khambhati, Ankit N.
Medaglia, John D.
Karuza, Elisabeth A.
Thompson-Schill, Sharon L.
Bassett, Danielle S.
author_facet Khambhati, Ankit N.
Medaglia, John D.
Karuza, Elisabeth A.
Thompson-Schill, Sharon L.
Bassett, Danielle S.
author_sort Khambhati, Ankit N.
collection PubMed
description Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions. To maneuver across an ever-changing landscape of mental states, the brain invokes cognitive control—a set of dynamic processes that engage and disengage different groups of brain regions to modulate attention, switch between tasks, and inhibit prepotent responses. Current theory posits that correlated and anticorrelated brain activity may signify cooperative and competitive interactions between brain areas that subserve adaptive behavior. In this study, we use a quantitative approach to identify distinct topological motifs of functional interactions and examine how their expression relates to cognitive control processes and behavior. In particular, we acquire fMRI BOLD signal in twenty-eight healthy subjects as they perform two cognitive control tasks—a Stroop interference task and a local-global perception switching task using Navon figures—each with low and high cognitive control demand conditions. Based on these data, we construct dynamic functional brain networks and use a parts-based, network decomposition technique called non-negative matrix factorization to identify putative cognitive control subgraphs whose temporal expression captures distributed network structures involved in different phases of cooperative and competitive control processes. Our results demonstrate that temporal expression of the subgraphs fluctuate alongside changes in cognitive demand and are associated with individual differences in task performance. These findings offer insight into how coordinated changes in the cooperative and competitive roles of cognitive systems map trajectories between cognitively demanding brain states.
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spelling pubmed-60560612018-08-03 Subgraphs of functional brain networks identify dynamical constraints of cognitive control Khambhati, Ankit N. Medaglia, John D. Karuza, Elisabeth A. Thompson-Schill, Sharon L. Bassett, Danielle S. PLoS Comput Biol Research Article Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions. To maneuver across an ever-changing landscape of mental states, the brain invokes cognitive control—a set of dynamic processes that engage and disengage different groups of brain regions to modulate attention, switch between tasks, and inhibit prepotent responses. Current theory posits that correlated and anticorrelated brain activity may signify cooperative and competitive interactions between brain areas that subserve adaptive behavior. In this study, we use a quantitative approach to identify distinct topological motifs of functional interactions and examine how their expression relates to cognitive control processes and behavior. In particular, we acquire fMRI BOLD signal in twenty-eight healthy subjects as they perform two cognitive control tasks—a Stroop interference task and a local-global perception switching task using Navon figures—each with low and high cognitive control demand conditions. Based on these data, we construct dynamic functional brain networks and use a parts-based, network decomposition technique called non-negative matrix factorization to identify putative cognitive control subgraphs whose temporal expression captures distributed network structures involved in different phases of cooperative and competitive control processes. Our results demonstrate that temporal expression of the subgraphs fluctuate alongside changes in cognitive demand and are associated with individual differences in task performance. These findings offer insight into how coordinated changes in the cooperative and competitive roles of cognitive systems map trajectories between cognitively demanding brain states. Public Library of Science 2018-07-06 /pmc/articles/PMC6056061/ /pubmed/29979673 http://dx.doi.org/10.1371/journal.pcbi.1006234 Text en © 2018 Khambhati et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Khambhati, Ankit N.
Medaglia, John D.
Karuza, Elisabeth A.
Thompson-Schill, Sharon L.
Bassett, Danielle S.
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
title Subgraphs of functional brain networks identify dynamical constraints of cognitive control
title_full Subgraphs of functional brain networks identify dynamical constraints of cognitive control
title_fullStr Subgraphs of functional brain networks identify dynamical constraints of cognitive control
title_full_unstemmed Subgraphs of functional brain networks identify dynamical constraints of cognitive control
title_short Subgraphs of functional brain networks identify dynamical constraints of cognitive control
title_sort subgraphs of functional brain networks identify dynamical constraints of cognitive control
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056061/
https://www.ncbi.nlm.nih.gov/pubmed/29979673
http://dx.doi.org/10.1371/journal.pcbi.1006234
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