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Dissertation Final_SHulbert.pdf (6.81 MB)

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Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement and Neurologic Rehabilitation

Hulbert, Sarah Marie, HULBERT
http://orcid.org/0000-0002-5121-1866
http://rave.ohiolink.edu/etdc/view?acc_num=osu1534678369235538

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Biophysics.
The neural control of movement provides a rich testing ground 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 for motor control during reaching. Biophysical approaches can also be applied to the prognosis of both traditional and gaming versions of a motor restorative treatment for human beings recovering from stroke. 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 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 constraint-induced movement therapy (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 & 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.
Hojjat Adeli, Ph. D. (Advisor)
John Buford, Ph. D. (Advisor)
Lynne Gauthier, Ph. D. (Committee Member)
168 p.
Biophysics
Motor Control; Neurorehabilitation; Neural Decoding; PMRF; Stimulus Triggered Averaging; EEG, Machine Learning;

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Citations

  • Hulbert, HULBERT, S. M. (2018). Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement and Neurologic Rehabilitation [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534678369235538

    APA Style (7th edition)

  • Hulbert, HULBERT, Sarah . Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement and Neurologic Rehabilitation. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1534678369235538.

    MLA Style (8th edition)

  • Hulbert, HULBERT, Sarah . "Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement and Neurologic Rehabilitation." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534678369235538

    Chicago Manual of Style (17th edition)

osu1534678369235538
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This open access ETD is published by The Ohio State University and OhioLINK.