de Villers-Sidani Étienne, Voss Patrice, Guitton Daniel, Cisneros-Franco J Miguel, Koch Nils A, Ducharme Simon
Innodem Neurosciences, Montreal, QC, Canada.
Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
Front Neurol. 2023 Jun 15;14:1204733. doi: 10.3389/fneur.2023.1204733. eCollection 2023.
The idea that eye movements can reflect certain aspects of brain function and inform on the presence of neurodegeneration is not a new one. Indeed, a growing body of research has shown that several neurodegenerative disorders, such as Alzheimer's and Parkinson's Disease, present characteristic eye movement anomalies and that specific gaze and eye movement parameters correlate with disease severity. The use of detailed eye movement recordings in research and clinical settings, however, has been limited due to the expensive nature and limited scalability of the required equipment. Here we test a novel technology that can track and measure eye movement parameters using the embedded camera of a mobile tablet. We show that using this technology can replicate several well-known findings regarding oculomotor anomalies in Parkinson's disease (PD), and furthermore show that several parameters significantly correlate with disease severity as assessed with the MDS-UPDRS motor subscale. A logistic regression classifier was able to accurately distinguish PD patients from healthy controls on the basis of six eye movement parameters with a sensitivity of 0.93 and specificity of 0.86. This tablet-based tool has the potential to accelerate eye movement research via affordable and scalable eye-tracking and aid with the identification of disease status and monitoring of disease progression in clinical settings.
眼动能够反映大脑功能的某些方面并提示神经退行性变的存在，这一观点并不新鲜。事实上，越来越多的研究表明，几种神经退行性疾病，如阿尔茨海默病和帕金森病，存在特征性的眼动异常，并且特定的注视和眼动参数与疾病严重程度相关。然而，由于所需设备价格昂贵且扩展性有限，在研究和临床环境中使用详细的眼动记录受到了限制。在此，我们测试了一种能够使用移动平板电脑的嵌入式摄像头跟踪和测量眼动参数的新技术。我们表明，使用该技术可以复制关于帕金森病（PD）眼动异常的几个著名发现，并且进一步表明，几个参数与通过MDS-UPDRS运动子量表评估的疾病严重程度显著相关。一个逻辑回归分类器能够根据六个眼动参数准确地区分PD患者和健康对照，灵敏度为0.93，特异性为0.86。这种基于平板电脑的工具有可能通过经济实惠且可扩展的眼动追踪加速眼动研究，并有助于在临床环境中识别疾病状态和监测疾病进展。
