Time-varying whole-brain functional network connectivity coupled to task engagement.

Brainwide integration Cognitive dynamics Cognitive marker Task-evoked connectivity dynamics Whole-brain connectivity pattern

Journal

Network neuroscience (Cambridge, Mass.)
ISSN: 2472-1751
Titre abrégé: Netw Neurosci
Pays: United States
ID NLM: 101719149

Informations de publication

Date de publication:
2019
Historique:
received: 18 10 2017
accepted: 16 03 2018
entrez: 23 2 2019
pubmed: 23 2 2019
medline: 23 2 2019
Statut: epublish

Résumé

Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using

Identifiants

pubmed: 30793073
doi: 10.1162/netn_a_00051
pii: netn_a_00051
pmc: PMC6326730
doi:

Types de publication

Journal Article

Langues

eng

Pagination

49-66

Subventions

Organisme : Intramural NIH HHS
ID : ZIA MH002783
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB006841
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB020407
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM103472
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH118695
Pays : United States

Déclaration de conflit d'intérêts

Competing Interests: The authors have declared that no competing interests exist.

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Auteurs

Hua Xie (H)

Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

Javier Gonzalez-Castillo (J)

Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Daniel A Handwerker (DA)

Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Peter A Bandettini (PA)

Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Vince D Calhoun (VD)

The Mind Research Network, Albuquerque, NM, USA.

Gang Chen (G)

Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Eswar Damaraju (E)

The Mind Research Network, Albuquerque, NM, USA.

Xiangyu Liu (X)

Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

Sunanda Mitra (S)

Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

Classifications MeSH