Neural Correlates of Countermanding Saccade Deficits in Parkinson's Disease

Abstract

Parkinson's Disease is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). The SNc supplies the basal ganglia (BG) via dopaminergic projections which innervate D1 and D2 receptors that mediate motor control. The BG also mediates cognitive processes and eye movement, parallel to its involvement in motor control. Behavioural correlates of PD have been established from previous countermanding tasks and population neural activity has been shown to correlate with PD disease state, but a reliable means to find patient-specific biomarkers of disease remains unknown. Here, we propose using eye movements and electroencephalography (EEG) to capture neural correlates of dysfunction in PD. We have developed a novel saccade-based stop-signal task in VR that probes the subject's ability to recruit the neural processes involved in action selection and response inhibition. We have tested this system on 7 healthy subjects and verified that we could identify key signature changes in the EEG profile during left and right saccade, countermand, and antisaccades similar to those found in similar reach tasks. The successful completion of a countermand (revoking a planned action) stop trial requires large synchronization of frontal theta and motor beta activity, representing the BG-thalamocortical loop recruiting the necessary processes to inhibit motor responses. The pattern in the event-related potentials that illustrates this is a strong event-related synchronization (ERS) peak followed by an event-related desynchronization (ERD) dip, and increased weights in the scalp topology at the frontal-parietal region. Since tasks involving response inhibition serve to probe the subject’s ability to revoke a planned action, it does not matter whether the task was completed using hand movements or saccades. Our ERP isolated from Independent Component Analysis (ICA) resembles the ERP from previous literature, and exhibits increased weights on the sensorimotor region with a narrow band beta. This narrow band beta range is subject-specific and can be better visualized by using a modelling approach called FOOOF (fitting oscillations one over f). Lastly, the increased decoding performance in each subject's successive recording session suggests that using subject-specific features positively biases the model towards enhanced generalizability. Our experimental platform provides a robust framework that accounts for trial-by-trial variability, and can capture the presence of and evoke beta oscillations in healthy subjects.