Technical Program

Paper Detail

Paper:	SA-PM-PS3.12
Session:	Functional, Dynamic and Parametric Imaging
Time:	Saturday, April 8, 13:30 - 14:50
Presentation:	Poster
Title:	Dynamic Bayesian Networks (DBNs) Demonstrate Impaired Brain Connectivity During Performance of Simultaneous Movements in Parkinson's Disease
Authors:	Junning Li; University of British Columbia
	Z. Jane Wang; University of British Columbia
	Martin McKeown; University of British Columbia
Abstract:	Many symptoms of brain diseases may be caused by altered connectivity between brain regions, necessitating the development of suitable models for inferring effective connectivity in fMRI. Here inspired by recent graphical approaches for inferring connectivity, we propose Dynamic Bayesian Networks (DBNs) for learning the effective connectivity between a priori specified brain regions of interest (ROIs). We applied this method to fMRI data from Parkinson's Disease (PD) and normal subjects performing a simultaneous movement task. Compared to the normal subject, the effective connectivity between motor regions was severely impaired in the PD subject, which was minimally ameliorated with L-dopa medication. These results imply a functional disconnection between brain regions far downstream from the basal ganglia, the initial site of pathology in PD. We suggest that DBNs provide a powerful framework to assess functional connectivity in fMRI studies of brain pathologies.

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