Constructing brain functional networks from EEG: partial and unpartial correlations.

Détails

ID Serval
serval:BIB_53BD2FF74AD1
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Constructing brain functional networks from EEG: partial and unpartial correlations.
Périodique
Journal of Integrative Neuroscience
Auteur(s)
Jalili M., Knyazeva M.G.
ISSN
0219-6352 (Print)
ISSN-L
0219-6352
Statut éditorial
Publié
Date de publication
2011
Volume
10
Numéro
2
Pages
213-232
Langue
anglais
Résumé
We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial correlations, which are often expensive to compute, reduce predicting such edges. We suggest combining these alternative methods in order to have complementary information on brain functional networks.
Pubmed
Web of science
Création de la notice
04/07/2011 12:34
Dernière modification de la notice
20/08/2019 14:08
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