Diotaiuti Pierluigi, Marotta Giulio, Di Siena Francesco, Vitiello Salvatore, Di Prinzio Francesco, Rodio Angelo, Di Libero Tommaso, Falese Lavinia, Mancone Stefania
Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
Department of Human Sciences, Philosophy and Education, University of Salerno, 84084 Fisciano, Italy.
Brain Sci. 2025 Mar 31;15(4):362. doi: 10.3390/brainsci15040362.
(1) Background. Eye movement abnormalities are increasingly recognized as early biomarkers of Parkinson's disease (PD), reflecting both motor and cognitive dysfunction. Advances in eye-tracking technology provide objective, quantifiable measures of saccadic impairments, fixation instability, smooth pursuit deficits, and pupillary changes. These advances offer new opportunities for early diagnosis, disease monitoring, and neurorehabilitation. (2) Objective. This narrative review explores the relationship between oculomotor dysfunction and PD pathophysiology, highlighting the potential applications of eye tracking in clinical and research settings. (3) Methods. A comprehensive literature review was conducted, focusing on peer-reviewed studies examining eye movement dysfunction in PD. Relevant publications were identified through PubMed, Scopus, and Web of Science, using key terms, such as "eye movements in Parkinson's disease", "saccadic control and neurodegeneration", "fixation instability in PD", and "eye-tracking for cognitive assessment". Studies integrating machine learning (ML) models and VR-based interventions were also included. (4) Results. Patients with PD exhibit distinct saccadic abnormalities, including hypometric saccades, prolonged saccadic latency, and increased anti-saccade errors. These impairments correlate with executive dysfunction and disease progression. Fixation instability and altered pupillary responses further support the role of oculomotor metrics as non-invasive biomarkers. Emerging AI-driven eye-tracking models show promise for automated PD diagnosis and progression tracking. (5) Conclusions. Eye tracking provides a reliable, cost-effective tool for early PD detection, cognitive assessment, and rehabilitation. Future research should focus on standardizing clinical protocols, validating predictive AI models, and integrating eye tracking into multimodal treatment strategies.
（1）背景。眼球运动异常越来越被认为是帕金森病（PD）的早期生物标志物，反映了运动和认知功能障碍。眼动追踪技术的进步提供了扫视障碍、注视不稳定、平稳跟踪缺陷和瞳孔变化的客观、可量化测量方法。这些进展为早期诊断、疾病监测和神经康复提供了新机会。（2）目的。本叙述性综述探讨了眼球运动功能障碍与PD病理生理学之间的关系，强调了眼动追踪在临床和研究环境中的潜在应用。（3）方法。进行了全面的文献综述，重点关注同行评审的研究，这些研究考察了PD中的眼球运动功能障碍。通过PubMed、Scopus和Web of Science使用关键词，如“帕金森病中的眼球运动”、“扫视控制与神经退行性变”、“PD中的注视不稳定”和“用于认知评估的眼动追踪”，识别相关出版物。还纳入了整合机器学习（ML）模型和基于虚拟现实（VR）干预的研究。（4）结果。PD患者表现出明显的扫视异常，包括扫视幅度减小、扫视潜伏期延长和反扫视错误增加。这些障碍与执行功能障碍和疾病进展相关。注视不稳定和瞳孔反应改变进一步支持了眼球运动指标作为非侵入性生物标志物的作用。新兴的人工智能驱动的眼动追踪模型显示出自动诊断PD和跟踪疾病进展的前景。（5）结论。眼动追踪为早期PD检测、认知评估和康复提供了一种可靠、经济高效的工具。未来的研究应侧重于标准化临床方案、验证预测性人工智能模型以及将眼动追踪整合到多模式治疗策略中。
