Eye movement and pupillary response abnormalities measured using virtual reality as biomarkers in the diagnosis of early-stage Parkinson’s disease - Zhao, Jing; Shi, Chong; Zhang, Xucheng; Ma, Shaochen; Sun, Wei; Tian, Feng; Wang, Peifu; Li, Jilai; Du, Jichen; Zhao, Xingquan; Wan, Zhirong (Frontiers in Neurology, 2025)
ObjectiveCharacteristic ocular symptoms are expected to serve as potential biomarkers for early diagnosis of Parkinson’s disease (PD). However, possible ocular impairments in PD patients are rarely studied. The study aimed to investigate eye movement characteristics and pupil diameter changes in early-stage PD patients using virtual reality (VR)-based system and explore their contribution in the diagnosis of early-stage PD.MethodsForty-three early-stage PD patients and 25 healthy controls were included. Eye movements and pupillary response of all subjects were recorded and evaluated by wearing VR glasses. All subjects completed pro-saccade and anti-saccade tasks. Saccadic eye movement and pupillary response parameters were analyzed. Random Forests method was used for classification task, the performance of the classification model in differentiating early-stage PD patients from healthy controls were evaluated.ResultsPD patients exhibited reduced pro-saccade velocity and accuracy, longer average time to complete the pro-saccade, and lower anti-saccade error correction rate than healthy controls (all p < 0.05). Significant differences were found in the trajectories of changes in pupil diameter between the two groups. After extraction of frequency-amplitude features of pupil constriction from the spectra of the eye movement signals of PD patients, it can be seen that the amplitudes of movement signals of both the left and right eyes at different frequencies during pro-saccade and anti-saccade tasks were significant. The number of significant amplitude frequencies in both eyes at low (0–6 Hz), medium (7–12 Hz) and high frequencies (13–19 Hz) was 23, 9, and 16, respectively, during pro-saccade task, which was 10, 29, and 43, respectively, during anti-saccade task. The model with all features achieved an accuracy of up to 79%.ConclusionThis study presents a non-invasive approach toward the diagnosis of early-stage PD with VR technology. Eye movement and pupillary response abnormalities measured using VR may be used as effective biomarkers for the diagnosis of early-stage PD.