Directed Brain Network Modeling Using Time Series Casualty Analysis

Undergraduate Research Project
Directed Brain Network Modeling Using Time Series Casualty Analysis
Project Description
The human brain is among the most complex systems known to mankind. Though there have been enormous studies
on brain functions, the dynamical transitions to neurological dysfunctions of brain disorders are still far from being
well understood in current neuroscience research [1, 2]. There has been a shift in understanding of functional brain
activity in the cerebral cortex in recent years. The older concept of a highly localized hierarchical structure that forms
the intervening steps between stimulus and response has recently given way to the notion of a distributed network of
modules with intrinsic properties that integrate in the presence of external stimuli [3, 4]. Accordingly, the intrinsic
architecture of connections forms a key component of cortical organization. This concept has motivated analyses that
enable us to delineate the directed brain network connectivity in vivo and to assess how neural activity dynamically
evolves along these structural links.
In this project, we will investigate some time series casualty
analysis methods, and apply them to construct directed brain
network flows. Instead of using conventional non-invasive
brain imaging approaches, this project will analyze corticocortical evoked potentials (CCEPs), which directly measure
local neural activity from inside of the brain to map dynamic
directed brain connectivity via electrical stimulation. The
CCEP data can measure dynamical brain activity in vivo,
which is not possible using scalp EEG, MEG, or fMRI. Using
time series casualty analysis, the students will investigate how
brainwave of an electrical stimulus spread over the brain. In
our previous study, we have already constructed a
computational framework to visualize and analyze directed
brain networks in Matlab, while the directed network flow A demonstration of the placement of the electrodes
construction procedure needs more investigation and to collect CCEP signals.
optimization. The students will make an extensive study on
bivariate time series casualty analysis methods, and apply it to construct directed brain network flows. The student
will learn to integrate time series casualty information with brain spatial information to quantify the structure of
directed brain network and also evaluate the stability of the brain connectivity models. This project will help facilitate
brain network analysis and will contribute a brain state identification framework, which can help neurologists to
identify dysregulated functional connectivity and gain a greater understanding of the brain networks associated with
some brain diseases.
Project Advisor:
Shouyi Wang, Ph.D.
Assistant Professor
Department of Industrial and Manufacturing Systems Engineering
University of Texas at Arlington
500 West First Street, Arlington, TX 76019
Office: Woolf Hall 420H
Tel: 817-272-2921
Fax: 817-272-3406
[1] W. H. O. (WHO), Epilepsy: historical overview, 2004,
[2] Epilepsy Foundation, Epilepsy foundation - not another moment lost to seizures, 2006,
[3] Raichle, M.E., A paradigm shift in functional brain imaging. J. Neurosci. 29(12): 729–734, 2009.
[4] Sporns, O., Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15: 247–262, 2013.