Statistics and Data Science Seminar
Prof. Abhyuday Mandal
University of Georgia
Social Network Models for Identifying Active Brain Regions from fMRI Data
Abstract: Functional magnetic resonance imaging (fMRI) is an important tool for
scientists studying brain function. FMRI data are complex in nature: they
are massive in size and a low signal-to-noise level makes the elimination of
some noise prior to model fitting desirable for improved identification of
true brain activity. We propose two methods of reducing this noise:
generalized indicator functional analysis and a hidden Markov model. Brain
regions showing increased fMRI signal while subjects engaged in a
visual/spatial motor task are identified using concepts from social network
analysis and statistical mechanics. Conditional probabilities of activation
given the degree to which pairs of voxels are related are modeled for three
groups: people with schizophrenia, their asymptomatic relatives, and control
subjects. We compare the conditional probability maps obtained for each
group to evaluate for between-group differences in extent of task-related
signal.
(Joint research with Ana M. Bargo, Lynne Seymour, Jennifer McDowelly, and
Nicole A. Lazar)
Friday April 9, 2010 at 2:00 PM in SEO 612