Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture.
Adult
Algorithms
Behavior
/ physiology
Brain Mapping
Cerebral Cortex
/ diagnostic imaging
Cluster Analysis
Connectome
Databases, Factual
Female
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Models, Neurological
Nerve Net
/ diagnostic imaging
Neural Pathways
/ physiology
Sensation
/ physiology
Young Adult
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
09
09
2019
accepted:
03
09
2020
pubmed:
21
10
2020
medline:
9
2
2021
entrez:
20
10
2020
Statut:
ppublish
Résumé
Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs 'edge time series' and 'edge functional connectivity' (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits.
Identifiants
pubmed: 33077948
doi: 10.1038/s41593-020-00719-y
pii: 10.1038/s41593-020-00719-y
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1644-1654Subventions
Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States
Commentaires et corrections
Type : CommentIn
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