Doctor of Philosophy
Electrical and Computer Engineering
Patel, Rajni V.
Jog, Mandar S.
Non-motor symptoms such as perceptual deficits and cognitive impairments, i.e., deficits in executive functions, presented at an early stage of Parkinson’s Disease (PD) substantially affect a PD patient’s quality of life and may contribute to motor impairments. Studies have emphasized the need to better understand these impairments and the abnormalities contributing to them as it provides a means to efficiently manage the disease. Further, due to the early onset of these deficits, the contributing abnormalities may be considered a potential biomarker for early diagnosis of PD. However, the impairments and the contributing abnormalities are not yet fully understood, leading to inadequate options to efficiently manage the disease. The Basal Ganglia, the region affected by PD, plays a vital role in Sensorimotor Integration (SMI) and Sensorimotor Control (SMC) functions – two fundamental processes involved in sensory perception and movement planning. The hypothesis is that the impairments in SMI and SMC contribute to deficits in perception and executive functions, leading to motor deficits and these impairments may be altered due to medication. The primary contribution of the thesis is the development of robotic tools for characterizing the SMI and SMC impairments in PD patients. The study’s results showed that PD patients suffer from an impaired SMI and SMC circuit that adversely affects multi-sensory integration, movement planning, online error correction, and execution of voluntary movements. Additionally, the findings have shown that dopaminergic medication significantly worsens SMI and SMC impairments. The secondary contribution is the development of a musculoskeletal model that can accurately estimate in-depth SMC features. The developed model may be used to guide and enhance the efficacy of PD-related therapies. The novel findings of the study contribute to advancing our knowledge about the disease and the effect of medication by characterizing the SMI and SMC impairments and demonstrating their contribution to deficits in perception, executive functions, and motor performance. The study’s results enable us to better target these deficits through efficient treatment optimization. Further, the thesis describes the development and validation of tools to effectively diagnose, monitor, and individualize the assessment of SMI, SMC, and, consequently, the corresponding non-motor impairments in PD.
Summary for Lay Audience
Parkinson’s Disease (PD) is one of the most common neurodegenerative disorders, known for its cardinal motor symptoms such as tremor, slowness of movement (bradykinesia), and rigidity. However, non-motor symptoms, such as impairments in perception and cognitive abilities, may be presented earlier than motor symptoms and can significantly affect the patient’s quality of life. Further, studies have reported that non-motor symptoms may contribute to the motor symptoms appearing later in the disease, making non-motor symptoms a promising biomarker for an early diagnosis. However, the nature of these non-motor symptoms and their contributors are not fully known, leading to inadequate options for managing these complex symptoms. Consequently, there is a need to better understand them to efficiently manage the disease. Sensorimotor Integration (SMI), which is responsible for accurately perceiving the world around us, and Sensorimotor Control (SMC), which is responsible for planning and execution of movements, have been hypothesized to be impaired in PD, leading to perceptual and cognitive abnormalities. Therefore, in this work, robotic tools were developed to explore the factors contributing to perceptual and cognitive abnormalities and the effect of medication. Multiple robot-based tasks were designed to examine the SMI and SMC performance in PD patients. It was found that the various aspects of SMI and SMC have been impaired in PD patients, leading to abnormalities in perception, and cognitive abilities, thereby affecting the patient’s ability to perform day-to-day tasks. Further, the dopaminergic medication has been found to worsen SMI and SMC impairments in PD patients, emphasizing the need to better optimize the treatment. A muscle model that can analyze in-depth SMC parameters was also developed as a potential tool to enhance the efficiency of PD therapies. The findings from the study provide valuable insights into factors contributing to specific non-motor impairments and the effect of medication, allowing us to target these impairments better. Additionally, owing to the lack of existing techniques to monitor non-motor impairments, robotic and simulation tools have been developed and validated. This may be considered a first step to using objective metrics in conjunction with existing clinical tools to better diagnose, monitor, and manage the disease.
Krishnasamy Tamilselvam, Yokhesh, "Investigation of Sensorimotor Integration and Control in Parkinson’s Disease using Haptics-enabled Robotics and Machine Learning" (2023). Electronic Thesis and Dissertation Repository. 9432.
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