Paper Info
Title
Conditional independence graphs for functional connectivity in functional MRI
Abstract
A recent concern in BOLD fMRI data analysis is extraction of connectivity information between regions. Functional connectivity and effective connectivity are the two notions defined so far, but the former lacks the ability to reveal direct interactions, while use of the latter requires the connectivity model to be set a priori. We propose that conditional independence graphs be considered as a proper way to extract and represent direct interactions between regions. We show how robust and fast statistical inference can be conducted on real data without need of any prior model.
Year
DOI
Venue
2002
10.1109/ISBI.2002.1029407
ISBI
Keywords
Field
DocType
biomedical MRI,brain,graphs,haemodynamics,medical image processing,BOLD fMRI data analysis,activated regions,activation detection,behavioral tasks,blood oxygen level dependence,brain hemodynamic changes,cognitive tasks,conditional independence graphs,functional MRI,functional connectivity,magnetic resonance imaging,medical diagnostic imaging,robust fast statistical inference
Graph,Pattern recognition,Conditional independence,Computer science,A priori and a posteriori,Statistical inference,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7803-7584-X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Guillaume Marrelec142629.12
M. Pelegrini-Issac2385.81
Habib Benali383768.94