Zhao Jing, Shi Chong, Zhang Xucheng, Ma Shaochen, Sun Wei, Tian Feng, Wang Peifu, Li Jilai, Du Jichen, Zhao Xingquan, Wan Zhirong
Department of Neurology, Aerospace Center Hospital, Beijing, China.
Department of Traditional Chinese Medicine, Aerospace Center Hospital, Beijing, China.
Front Neurol. 2025 Apr 23;16:1537841. doi: 10.3389/fneur.2025.1537841. eCollection 2025.
Characteristic 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.
Forty-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.
PD 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 < 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%.
This 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.
典型的眼部症状有望成为帕金森病（PD）早期诊断的潜在生物标志物。然而，PD患者可能存在的眼部损害很少被研究。本研究旨在使用基于虚拟现实（VR）的系统调查早期PD患者的眼动特征和瞳孔直径变化，并探讨它们在早期PD诊断中的作用。
纳入43例早期PD患者和25名健康对照者。所有受试者佩戴VR眼镜，记录并评估其眼动和瞳孔反应。所有受试者完成同向扫视和反向扫视任务。分析眼跳运动和瞳孔反应参数。采用随机森林方法进行分类任务，评估分类模型区分早期PD患者和健康对照者的性能。
与健康对照者相比，PD患者同向扫视速度和准确性降低，完成同向扫视的平均时间更长，反向扫视纠错率更低（均P<0.05）。两组瞳孔直径变化轨迹存在显著差异。从PD患者眼动信号频谱中提取瞳孔收缩的频率-幅度特征后可见，同向扫视和反向扫视任务期间，左右眼在不同频率下的运动信号幅度均有显著性。同向扫视任务期间，双眼在低（0-6Hz）、中（7-12Hz）、高频（13-19Hz）的显著幅度频率数分别为23、9、16，反向扫视任务期间分别为10、29、43。具有所有特征的模型准确率高达79%。
本研究提出了一种利用VR技术诊断早期PD的非侵入性方法。使用VR测量的眼动和瞳孔反应异常可能作为早期PD诊断的有效生物标志物。
