Speaker: Moo K. Chung, PhD
Associate Professor
Department of Biostatistics and Medical Informatics;
Waisman Laboratory for Brain Imaging and Behavior
University of Wisconsin-Madison
                 
Tuesday, February 7th
4:00 p.m.
Biotechnology Center Auditorium
425 Henry Mall

PERSISTENT HOMOLOGY AND ITS APPLICATION TO BRAIN NETWORK MODELING

Abstract:

     By borrowing heavily from persistent homology, topological data
analysis, tries to infer high dimensional structure by understanding how
local neighborhoods of the structure connect to each other to form a
topology. A simplical complex with a scale parameter usually represents the
local connectivity. Then instead of looking at the topology at a fixed
scale, the persistent homology observes the changes of topology over the
varying scales and finds the most persistent topological features, which are
robust under perturbation. The persistent homology has been previously used
as a form of topological feature reduction in quantifying amount of the gray
matter in the magnetic resonance images (MRI) of the human brain. Recently
we have succeeded in modeling the brain network by introducing the concept
of graph filtration, which decomposes an arbitrary weighted graph uniquely
into a sequence of unweighted graphs. The prosed method is applied in
characterizing autistic brain network using MRI, PET and DTI. We will also
present our recent attempt at extending the framework to scale spaces
induced by heat diffusion.

Brief Bio:

     Moo K. Chung, Ph.D. (http://www.stat.wisc.edu/~mchung), is an Associate
Professor in the Department of Biostatistics and Medical Informatics at the
University of Wisconsin-Madison. Also affiliated with the Waisman Laboratory
for Brain Imaging and Behavior. He is also affiliated with the Department of
Brain and Cognitive Sciences, Seoul National University since 2009. Dr.
Chung received Ph.D. in statistics from McGill University under Keith J.
Worsley and James O. Ramsay in 2001. Dr. Chung¹s main research area is
computational neuroanatomy, where noninvasive brain imaging modalities such
as magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) are
used to map the spatiotemporal dynamics of the human brain. In 2011, Dr.
Chung received the Editor's Award for best paper published in Journal of
Speech, Language, and Hearing Research, where CT images of vocal tract
structures are analyzed. Recent research focus has been on the topological
data analysis using persistent homology and its application to brain imaging
and networks. He is currently writing a 400-page research monograph on
computational neuroanatomy that will be published this year.