Directed Brain Network Modeling Using Time Series Casualty Analysis

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 cortico-cortical 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 construction procedure needs more investigation and 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 informationwithbrain spatial information toquantify 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 helpneurologists to identify dysregulated functional connectivity and gain a greater understanding of the brain networksassociated 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

Email:

References

[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.