Functional connectivity: the principal-component analysis of large (PET) data sets.

Détails

ID Serval
serval:BIB_DF007672F922
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Functional connectivity: the principal-component analysis of large (PET) data sets.
Périodique
Journal of Cerebral Blood Flow and Metabolism
Auteur⸱e⸱s
Friston K.J., Frith C.D., Liddle P.F., Frackowiak R.S.
ISSN
0271-678X (Print)
ISSN-L
0271-678X
Statut éditorial
Publié
Date de publication
1993
Volume
13
Numéro
1
Pages
5-14
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Résumé
The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.
Mots-clé
Algorithms, Brain/physiology, Cerebrovascular Circulation, Humans, Male, Neural Pathways/physiology, Neurophysiology
Pubmed
Web of science
Open Access
Oui
Création de la notice
25/09/2011 16:50
Dernière modification de la notice
20/08/2019 17:03
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