Tracking spatial dynamics of functional connectivity during a task.


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
01 10 2021
Historique:
received: 18 01 2021
revised: 15 05 2021
accepted: 23 06 2021
pubmed: 28 6 2021
medline: 29 10 2021
entrez: 27 6 2021
Statut: ppublish

Résumé

Functional connectivity (FC) measured from functional magnetic resonance imaging (fMRI) provides a powerful tool to explore brain organization. Studies of the temporal dynamics of brain organization have shown a large temporal variability of the functional connectome, which may be associated with mental status transitions and/or adaptive process. Most dynamic studies, e.g. functional connectome and functional network connectivity (FNC), have focused on the macroscopic FC changes, i.e. the changes of temporal coherence across various brain network sources, nodes and/or regions of interest, where it is assumed within the network or node that the FC is static. In this paper, we develop a novel method to examine the spatial dynamics of FC, without the assumption of its intra-network stationarity. We applied our approach to fMRI data during an auditory oddball task (AOD) from twenty-two subjects, in an attempt to capture/validate the approach by evaluating whether spatial connectivity varies with task condition. The results showed that connectivity networks exhibit spatial variability over time, in addition to participating in conventional temporal dynamics, i.e. cross-network variability or dynamic functional network connectivity (dFNC). Furthermore, we studied the relationship of spatial dynamic in FC to cognitive processes, by performing a cluster analysis to evaluate an individual's functional correspondence towards the 'target' (oddball) detection from AOD task, and extracting cognitive task correspondence states as well as their dynamic FC spatial maps segregated by such states. We found a clear trend in different task-guided states, particularly, a prominent reduction of task stimulus synchrony state along with strong anticorrelation between default mode network (DMN) and cognitive attentional networks. We also observed an increasing occurrence of the task desynchrony state which showed an absence of DMN anticorrelation. The results highlight the impact of a well-studied cognitive task on the observed spatial dynamic structure. We also showed that the FC spatial dynamic pattern from our method largely corresponds to macroscopic dFNC patterns, but with more details and specifications over space, meanwhile the connectivity within the source itself provides novel information and varies over time. Overall, we demonstrate clear evidence of the presence of the (usually ignored) spatial dynamics of connectivity, its links to the task and implications of cognition/mental status.

Identifiants

pubmed: 34175424
pii: S1053-8119(21)00586-3
doi: 10.1016/j.neuroimage.2021.118310
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

118310

Subventions

Organisme : NIBIB NIH HHS
ID : R01 EB005846
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB006841
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM103472
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB020407
Pays : United States

Informations de copyright

Copyright © 2021. Published by Elsevier Inc.

Auteurs

Lei Wu (L)

Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia Institute of Technology, Emory University, Georgia State University, Atlanta 30303, GA, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque 87131, Mexico. Electronic address: lwu9@gsu.edu.

Arvind Caprihan (A)

The Mind Research Network, Albuquerque 87106, Mexico.

Vince Calhoun (V)

Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia Institute of Technology, Emory University, Georgia State University, Atlanta 30303, GA, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque 87131, Mexico; The Mind Research Network, Albuquerque 87106, Mexico. Electronic address: vcalhoun@gsu.edu.

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Classifications MeSH