Novel advances in engineering and data analytics are revolutionizing both our ability to monitor Parkinson’s disease (PD) patient symptoms and our understanding of neuropathology. Despite promise, key challenges exist before patient monitoring technologies become standard in clinical settings, including 1) industry standardization of sensor-based analytical approaches; 2) validation of endpoint sensitivity to degree of impairment and medication state; and 3) consensus regarding appropriate devices, algorithms, data requirements, and statistical analysis requirements for symptom measurement outside of the clinic. In addition to the need for better patient monitoring, no disease-modifying therapeutics currently exist and thorough understanding of the neuropathology of PD remains elusive. To this end, large network brain simulations that leverage efficient computational frameworks are beginning to provide insight into mechanisms that facilitate pathological oscillations and may serve to identify new therapeutic targets.
To address current limitations in patient monitoring and our understanding of neuropathology, in this dissertation I 1) develop and evaluate validity and reliability of an open-source, wearable sensor-based algorithm for measuring gait in PD patients, 2) evaluate and compare sensitivity of at-home measurements relative to in-clinic measurements, 3) evaluate sensitivity of wearable-derived features for measuring degree of gait impairment and treatment response in PD patients, and 4) investigate the effect of synaptic parameters on beta synchrony and entrainment in a large-scale spiking model of the subthalamic nucleus-globus pallidus externa (STN-GPe) network of the basal ganglia. Importantly, I find that sensor-derived features derived from the at-home environment differ from and are more sensitive to small changes compared to in-clinic, traditional assessments. Furthermore, I demonstrate the capacity for a single, lower back sensor-based algorithm to estimate gait features with sufficient sensitivity to detect degree of gait impairment and treatment effect in a mild-to-moderate PD population. Lastly, I demonstrate that weak synaptic connections between STN and GPe allows the STN-GPe network to entrain to a wide range of frequencies outside of the beta range, thus elucidating constraints on conditions required for beta production. Together, my work provides new insights into the feasibility and benefits of sensor-based symptom monitoring and PD-related neuropathology.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44021 |
| Date | 14 March 2022 |
| Creators | Czech, Matthew |
| Contributors | Thomas, Kevin |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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