Topographic gradients of intrinsic dynamics across neocortex.
connectome
hierarchy
human
intrinsic dynamics
microstructure
network
neuroscience
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
17 12 2020
17 12 2020
Historique:
received:
14
08
2020
accepted:
16
12
2020
pubmed:
18
12
2020
medline:
19
3
2021
entrez:
17
12
2020
Statut:
epublish
Résumé
The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive
Identifiants
pubmed: 33331819
doi: 10.7554/eLife.62116
pii: 62116
pmc: PMC7771969
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Natural Sciences and Engineering Research Council of Canada
ID : RGPIN #017–04265
Pays : International
Informations de copyright
© 2020, Shafiei et al.
Déclaration de conflit d'intérêts
GS, RM, RV, BB, BF, BM No competing interests declared
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