Investigation of Sensorimotor Integration and Control in Parkinson’s Disease using Haptics-enabled Robotics and Machine Learning
Abstract
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.