Multi-scale integration and predictability in resting state brain activity.

Détails

Ressource 1Télécharger: BIB_72802B296F0E.P001.pdf (4230.09 [Ko])
Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_72802B296F0E
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Multi-scale integration and predictability in resting state brain activity.
Périodique
Frontiers in Neuroinformatics
Auteur(s)
Kolchinsky A., van den Heuvel M.P., Griffa A., Hagmann P., Rocha L.M., Sporns O., Goñi J.
ISSN
1662-5196 (Electronic)
ISSN-L
1662-5196
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Volume
8
Pages
66
Langue
anglais
Notes
Publication types: Journal Article Publication Status: epublish
Résumé
The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.
Pubmed
Web of science
Open Access
Oui
Création de la notice
15/10/2014 13:35
Dernière modification de la notice
20/08/2019 14:30
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