Date of Award


Degree Type


Degree Name

Master of Science in Interdisciplinary Neurosciences


Interdisciplinary Neuroscience

First Advisor

Kunal Mankodiya


Parkinson's disease (PD) is a neurodegenerative disorder. Researchers are investigating ways to identify neural and behavioral markers for PD that can lead to earlier diagnosis and more effective treatments. The goal of this research is to quantify the effects of motor tasks on corticokinematic coherence(CKC) in PD. We can consider this research as a proof of concept study. This research can eventually help us quantify the motor symptoms related to PD using the measurement process called CKC. Brain muscle synchrony can be quantified as corticomuscular coherence (CMC) and corticokinematic coherence (CKC). Surgical and Pharmacological treatments have not been shown to have consistent, positive effects on PD, although improvements in limb function have been reported. In this research, we studied neural responses during motor tasks using electroencephalography (EEG). Specifically, a finger tapping test which is widely used in motor screening exam such as Unified Parkinson's Disease Rating Scale - UPDRS was used at two different frequencies, with and without metronome support in maintaining the correct pacing frequency which has been found to influence perceptual processing by entraining endogenous neural oscillations. This allows for investigation of CKC variation between movement frequencies of 1 Hz and 2 Hz in participants. We had 10 neurotypical individuals and 4 People with PD (PwPD), of which we analyzed results from 8 neurotypical individuals and 3 PwPD. Both groups showed prominent CKC at the frequency of finger tapping in the contralateral sensorimotor cortex. We also explored mu rhythm suppression as a result of finger tapping using wavelet-based time-frequency analysis. The use of a Smart Glove with Flex sensors which is explicitly designed to measure subtle irregularities in finger kinematics was an additional novel approach towards measuring CKC in people with Parkinson's which allows comparisons of neural activity at the finger tapping frequencies and thereby can be helpful in quantifying the motor-related symptoms associated with Parkinson. This is a first of its kind of study that investigates synchrony between neural oscillations and finger kinematics recorded by Smart Glove Flex sensors paced by auditory cue.



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