Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance

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dc.citation.articleNumber420
dc.citation.journalTitleFrontiers in Human Neuroscience
dc.citation.volumeNumber11
dc.contributor.authorRamos-Nuñez, Aurora I.
dc.contributor.authorFischer-Baum, Simon
dc.contributor.authorMartin, Randi C.
dc.contributor.authorYue, Qiuhai
dc.contributor.authorYe, Fengdan
dc.contributor.authorDeem, Michael W.
dc.date.accessioned2017-09-19T14:07:18Z
dc.date.available2017-09-19T14:07:18Z
dc.date.issued2017
dc.description.abstractIn cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity—modularity and flexibility—which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.
dc.identifier.citationRamos-Nuñez, Aurora I., Fischer-Baum, Simon, Martin, Randi C., et al.. "Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance." <i>Frontiers in Human Neuroscience,</i> 11, (2017) Frontiers Media S.A.: https://doi.org/10.3389/fnhum.2017.00420.
dc.identifier.digitalStatic_Dynamic_Measures_Human_Brain_Connectivity
dc.identifier.doihttps://doi.org/10.3389/fnhum.2017.00420
dc.identifier.urihttps://hdl.handle.net/1911/97400
dc.language.isoeng
dc.publisherFrontiers Media S.A.
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.keywordbrain network connectivity
dc.subject.keywordflexibility
dc.subject.keywordindividual differences
dc.subject.keywordmodularity
dc.subject.keywordresting-state fMRI
dc.subject.keywordtask complexity
dc.titleStatic and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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