A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks.

A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks. Research Abstract Details 

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A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks. Abstract Text:

Junning Li,Z Wang,Martin J McKeown,Junning Li,Z Jane Wang,Martin J McKeown,

FMRI experiments are usually performed to make inferences about groups of subjects, but current group analysis methods for dynamic Bayesian networks (DBNs) do not easily allow incorporation of covariates of interest. In this paper, we propose a group-analysis method which uses multivariate analysis of variance (MANOVA) to address this issue. The method is performed in two stages: first, deriving a DBN connectivity network among brain regions for each subject separately; second, regressing the connectivity coefficients of DBNs to the factors of interest and performing MANOVA. A case study involving fMRI data from Parkinson's disease (PD) subjects yields promising results. Ten out of the thirteen potential connections between Regions of Interest (ROIs) which are associated with disease state are functionally improved after medication (Table I), consistent with clinical observations. The results confirm that improvement in PD symptoms after medications is in part mediated by enhanced functional brain connectivity between brain regions.

A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks. Publishing Authors By Initials

J Li,Z Wang,MJ McKeown,J Li,ZJ Wang,MJ McKeown,

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A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Conference proceedings : ... Annual International

VOLUME: 1

Page Numbers: 5991-4

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ISSN: 1557-170X

DAY: 16

MONTH: 11

YEAR: 2007

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LANGUAGE: eng

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AFFILIATION: Department of Electrical and Computer Engineering, Brain Research Centre, University of British, Columbia, Canada.

Country: United States

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MEDLINETA: Conf Proc IEEE Eng Med Biol So

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