Date of Award

12-2001

Document Type

Thesis

Degree Name

Master of Science (MS)

Program

Biomedical Engineering

Research Advisor

Lawrence Jordan, Ph.D.

Committee

Joseph Green, M.D. Jack Buchanan, M.D., M.S.E.E. Paul Herron, Ph.D.

Keywords

EEG, MRI, spinal cord injury, motor potential, primary motor area, neuroplasticity, source analysis, t-test

Abstract

The annual incidence of spinal cord injury (SCI), not including those who die at the scene of the accident, is approximately 10,000 new cases in the United States. SCI, in its best outcome, may partially and temporarily disconnect the spinal cord from the brain. Some neuronal pathways remain intact in most S CI individuals, whose recovery depends on the utilization of the surviving connections. There is a change in the control of voluntary movements of the extremities by the cerebral cortex of the brain following spinal cord injury.

The technology of high-resolution EEG co-registered with MRI was applied to non-invasively investigate the brain’s movement control network in both SCI and normal subjects. A series of active and passive movement tests were carried out to explore the changes that o ccur in the brain’s cortical motor control after SCI. The spatial location of the brain areas active during motor tasks was identified in each individual and a statistical analysis was performed. It was found that activation of the motor cortex in SC I patients originated from a posterior part of the brain compared to the normal controls. The spatial difference was found to be statistically significant in the two groups with the p-values less than 0.05 in both active and passive movement tests.

We trust this study will contribute to the understanding of how the brain reorganizes its motor pathways after SCI. The clinical goal is the maximum utilization of the surviving connections to improve patient recovery. Also, understanding the neurona l activity and its topography in the brain is important in view of recent advances in experiments on primates. EEG can serve as an interface between the brain and computer-driven prostheses.

DOI

10.21007/etd.cghs.2001.0270

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