Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring

Advisor: Mark A. Clements, Sc.D. (Georgia Tech)

 Committee:

Thomas Wichmann, M.D. (Emory)
Garrett Stanley, PhD (Georgia Tech/Emory University)
Chris Rozell, PhD (Georgia Tech)
Beth Buffalo, PhD (University of Washington)

This research presents signal processing applications for extracting information from brain electrophysiology and movement signals. The approach taken does not assume any particular stimulus, underlying activity, or synchronizing event, nor does it assume any particular encoding scheme. Instead, novel signal processing applications of complex continuous wavelet transforms, cross frequency coupling, feature selection, and canonical correlation were developed to discover the most signifi cant electrophysiologic changes in the basal ganglia and cortex of parkinsononian rhesus monkeys and how these changes are related to the motor signs of parkinsonism. The resulting algorithms e ffectively decode the parkinsonian disease state and, when combined with motor signal decoding algorithms, allow technology-assisted multi-modal grading of the disease. Based on these results, parallel data collection algorithms were implemented in real-time embedded software and o ff-the-shelf hardware to develop a new system to facilitate monitoring of the severity of Parkinson's disease signs and symptoms.

Off-line analysis of data collected with the system was subsequently shown to allow discrimination between normal and parkinsonian conditions in human subjects.

A suite of signal processing algorithms designed for decoding neural disease states, along with new insights gained by applying these tools to understanding parkinsonism, are presented. The main contributions of this work are in three areas: 1) Evidence of the importance of optimally selecting multiple, nonredundant features for understanding neural information, 2) Discovery of signifi cant correlations between certain motor signs and brain electrophysiology in di fferent brain regions, and 3) Implementation and human subject testing of multi-modal monitoring technology.