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Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement And Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement and Neurologic Rehabilitation Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Sarah Hulbert Graduate Program in Biophysics The Ohio State University 2018 Dissertation Committee John Buford, Advisor Hojjat Adeli, Co-Advisor Lynne Gauthier, Committee Member Copyrighted by Sarah Hulbert 2018 2 Abstract The neural control of movement provides a rich testing ground for principles and approaches from the field of biophysics. Multiple centers of the brain take part in motor control. A major focus of this dissertation is on areas crucial for planning and performance of skilled reaching, the primary motor cortex, the supplementary motor area, and the pre-motor area of the cerebral cortex, and the pontomedullary reticular formation (PMRF) in the brain stem. Neurophysiological studies are based around the electrical signals present in neurons and include techniques to record these signals and to use electricity to stimulate the nervous system to produce responses. One study in this dissertation shows that both simultaneous and offset stimulations of cortical motor areas and the PMRF produced EMG responses in the arms. Some of these patterns were indicative of simple summation of outputs from the cortical and brainstem sites, but there were also responses indicating gating of the effects from one site by the other, and even more complex interactions between the motor outputs. This suggests that the cortex and brainstem utilize a variety of pathways cooperatively for motor control during reaching. Biophysical approaches can also be applied to the prognosis of both traditional and a gaming version of a motor restorative treatment for human beings recovering from stroke, which is called Constraint-Induced Movement Therapy (CI therapy). A second study in this dissertation shows that, by utilizing machine learning approaches, the prognosis can be determined with high accuracy from pre-therapy scores of motor function, as indicated by the Wolf Motor Function ii Test. In the manner that was investigated as part of this dissertation, utilizing machine learning and specifically an Enhanced Probabilistic Neural Network, pre-therapy somatosentation did not increase the accuracy of prognosis. However, a more thorough investigation of specific facets of motor function as measured by the Wolf Motor Function Test found that baseline gross motor ability is a better predictor of therapy outcomes than baseline fine motor ability. Moreover, individuals with poor gross motor ability at pre-therapy baseline demonstrated a more beneficial rehabilitation response in both gaming and traditional CI therapy. This suggests that a person’s baseline gross motor ability may be useful as a supplementary factor in predicting which type of therapy (CI or not) is best for that person. Finally, this dissertation shows that by exploiting information contained within electrical brain signals (EEG), movement characteristics, specifically quality of movement, can be extracted from features in the frequency domain of EEG data captured during performance of gaming CI therapy by a person with mild hemi-paresis. Unlike previous studies that have exploited features indicative of movement type, this study reveals a more nuanced characteristic of the signal that can be extracted. With the ability to predict the quality of movement, this information could be used for personalized feedback during motor restorative therapy. After a brief introduction and background (chapters 1 and 2), each of these findings will be presented in the subsequent chapters (3-6). Chapter 7 will conclude the dissertation with a synthesis of these results and future directions iii Dedication To my husband, who selflessly supported me in this endeavor. To my family who removed all barriers to help me succeed. To my friends, my Columbus family, who never wavered in their steadfast support. To my advisors, Dr. Hojjat Adeli and Dr. John Buford, and committee member, Dr. Lynne Gauthier, for their irreplaceable guidance and mentorship. iv Acknowledgments I would like to express my sincerest gratitude toward my Advisors, Dr. Hojjat Adeli and Dr. John Buford for their guidance and mentorship throughout this entire process. Dr. Adeli, your encouragement for me to pursue something I am passionate about, and to do so through collaboration, is among the best advice I have received. It has allowed me to create meaningful research that also has a home in my heart. The wisdom and expertise you passed on to me concerning how to write and communicate research and the resources you shared with me concerning the most state-of-the-art computational techniques have been invaluable assets to my success. Dr. Buford, I am forever thankful for your willingness to see my potential for growth and your patience as I tried and tested different things. I am also very appreciative for your willingness to always meet, each time coming to the table ready to make progress and offer guidance, regardless of how much time had passed. Above all, I am grateful for your support and words of encouragement throughout this process. In this way, you instilled in me a confidence that I did not have when I came to graduate school. Because of this, I am able to approach science with a discerning eye and defend my own work. Thank you also to my committee member, Dr. Lynne Gauthier, for your guidance and mentorship in the neurorecovery and brain imaging lab. You pushed the limits of my programming skills and my ability to critically think through scientific projects and, in this way, v you fostered my growth as a scientist. You also gave me the opportunity to work with and for people to find solutions for them. In this way, you helped me realize some of my true passions in life. So, to my committee, I am truly thankful to have had the opportunity to work closely with not one, but three experts throughout my graduate school career. I am shaped by what you have taught me and am forever grateful. Thank you also to my fellow Ph.D. students and lab partners, Alexis Ortiz-Rosario, Ph.D., Alexis Burns, and Mohammad Rafiei, Ph.D.. Your support and guidance were invaluable throughout this process. I enjoyed every day working side-by-side with you. I would also like to thank, from the motor systems and neurophysiology lab, Tom Hirschauer, MD, PhD, lab technical staff , Amada Jellick and Rebecca Slattery, as well as previous students in the lab group who help initiate these studies, Wendy Herbert, PhD, PT, Lynnette Montgomery, PhD, Jacob Banks, MS, and a previous lab tech, Stephanie Moran. Special thanks to the professionals in ULAR at OSU who took such wonderful care of the subjects. This work was supported in part by NIH R01 NS 037822, and by grants from the provost’s office at Ohio State Thank you also to the other members of the neurorecovery and brain imaging lab, Dr. Alexandra Borstad, Troy Richter, Kris Kelly, Carson Herron, and all of the amazing undergraduate students I had the opportunity to work with. Thank you to the Engineering Education Department for funding part of my stay of the Ohio State University and giving me the opportunity to explore my love of teaching as well as research. vi Finally, thank you to my husband, Jarren. Your support has been incomparable. You are my rock and the purest form of support. To my family who cheered for me and supported me from start to finish and never wavered in their belief that I could do this. And to my Ohio family; I came to Ohio without knowing anyone and now I leave the closest and dearest, most steadfast friends I have ever had. I could not have done this without you. vii Vita May, 2016 …………………………......…M.S. in Biophysics, The Ohio State University April, 2013………………....…………….. B.S. in Physics, Western Michigan University December, 2015…………….……….Associates in Science, Kellogg Community College April, 2016 – Present..............................Graduate Research Associate; Neuroimaging and Neurorecovery Lab, The Ohio State University June, 2013-Present…………………….Graduate Research Associate; Motor Systems and Neurophysiology Lab, The Ohio State University August 2015-August, 2016……...…………Graduate Teaching Associate, The Ohio State University, Department of Engineering Education August 2016-May, 2018………….….Lead Graduate Teaching Associate, The Ohio State University, Department of Engineering Education viii Publications George, S. H., Rafiei, M. H., Borstad, A., Adeli, H., & Gauthier, L. V. (2017). Gross motor ability predicts response to upper extremity rehabilitation in chronic stroke. Behavioural Brain Research, 333, 314-322. George, S. H., Rafiei, M. H., Gauthier, L., Borstad, A., Buford, J. A., & Adeli, H. (2017). Computer-aided prediction of extent of motor recovery following constraint-induced movement therapy in chronic stroke. Behavioural Brain Research, 329, 191-199. Hulbert, S., & Adeli, H. (2015). Spotting psychopaths using technology. Reviews in the neurosciences, 26(6), 721-732. Hulbert, S., & Adeli, H. (2013). EEG/MEG-and imaging-based diagnosis of Alzheimer’s disease. Reviews in the neurosciences, 24(6), 563-576. Fields of Study Major Field: Biophysics ix Table of Contents Abstract ............................................................................................................................... ii Dedication .........................................................................................................................
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