Oculomotor function captured via an electronic tablet informs on cognition and disease severity in Parkinson’s Disease

Abstract

Abstract Studying the oculomotor system provides a unique opportunity and window to assess brain health and function in various clinical populations. Although the use of detailed oculomotor parameters in clinical research has been limited due to the scalability of the required equipment, the development of novel tablet-based eye-tracking technologies has created opportunities for reliable and accurate eye tracking measures. Oculomotor measures captured via a mobile tablet-based technology have previously been shown to reliably discriminate between Parkinson’s Disease (PD) patients and healthy controls. Here we further the use of oculomotor measures from tablet-based eye-tracking to inform on various cognitive abilities and disease severity in PD patients. When combined using partial least square regression, the extracted oculomotor parameters can explain up to 71% of the variance in cognitive test scores (e.g. Trail Making Test). Moreover, using a receiver operating characteristics (ROC) analysis we show that eye-tracking parameters can be used in a support vector classifier to discriminate between individuals with mild PD from those with moderate PD (based on UPDRS cut-off scores) with an accuracy of 89%.