Functional connectivity: the principal-component analysis of large (PET) data sets.
Details
Serval ID
serval:BIB_DF007672F922
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Functional connectivity: the principal-component analysis of large (PET) data sets.
Journal
Journal of Cerebral Blood Flow and Metabolism
ISSN
0271-678X (Print)
ISSN-L
0271-678X
Publication state
Published
Issued date
1993
Volume
13
Number
1
Pages
5-14
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Abstract
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.
Keywords
Algorithms, Brain/physiology, Cerebrovascular Circulation, Humans, Male, Neural Pathways/physiology, Neurophysiology
Pubmed
Web of science
Open Access
Yes
Create date
25/09/2011 16:50
Last modification date
20/08/2019 17:03