8月25日，《神经学杂志》（Journal of Neurology）在线发表的一项研究中，研究人员声称，由于注意力缺陷多动障碍（ADHD），胎儿酒精谱系障碍（FASD）和帕金森病（PD）都涉及眼控制和注意功能障碍，通过评价患者看电视时怎样移动他们的眼睛，可轻松诊断出这些疾病。
“自然的关注和眼球运动行为 - 就像一滴唾液 - 包含个体以及他/她的脑功能或功能障碍状态的生物特征识别”，文章指出， “然而，这样的个体特征，特别是神经系统疾病可能含有潜在的生物标志物，尚未被成功解码。”
典型的检测方法——临床评价、结构化行为任务、神经影像，花费人力、物力，并且受患者理解力和依从性的限制。为了解决这个问题， 南加州大学维特比工程学院计算机科学系Po-He Tseng 博士生和Laurent Itti教授与加拿大Queen大学的合作者，已经设计出一种新的筛查方法。
参与者，遵循简单指示“观看和享受”20分钟的电视短片，记录他们的眼动。眼球追踪数据，然后再结合规范的眼球追踪数据和视觉注意计算模型提取的224个量化特征，来确定区别于对照组患者的关键特征。
结合108例的眼动数据得出，该小组能够识别帕金森病的老年人有89.6％的准确率，儿童多动症或FASD 有77.3％的准确率。
该团队的方法在关注和凝视控制是影响特定的疾病这方面提供了新的见解，提供了相当有前景的，容易实施，低成本的，高通量筛选工具，特别是对于传统测试依从性低的年幼的儿童和老年人。
“这是我们第一次通过个人的日常行为，精确破译他们的神经状态”Itti 说道。（生物谷Bioon.com）
doi:10.1007/s00415-012-6631-2
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PMID:
High-throughput classification of clinical populations from natural viewing eye movements.
Tseng PH, Cameron IG, Pari G, Reynolds JN, Munoz DP, Itti L.
Many high-prevalence neurological disorders involve dysfunctions of oculomotor control and attention, including attention deficit hyperactivity disorder (ADHD), fetal alcohol spectrum disorder (FASD), and Parkinson's disease (PD). Previous studies have examined these deficits with clinical neurological evaluation, structured behavioral tasks, and neuroimaging. Yet, time and monetary costs prevent deploying these evaluations to large at-risk populations, which is critically important for earlier detection and better treatment. We devised a high-throughput, low-cost method where participants simply watched television while we recorded their eye movements. We combined eye-tracking data from patients and controls with a computational model of visual attention to extract 224 quantitative features. Using machine learning in a workflow inspired by microarray analysis, we identified critical features that differentiate patients from control subjects. With eye movement traces recorded from only 15 min of videos, we classified PD versus age-matched controls with 89.6 % accuracy (chance 63.2 %), and ADHD versus FASD versus control children with 77.3 % accuracy (chance 40.4 %). Our technique provides new quantitative insights into which aspects of attention and gaze control are affected by specific disorders. There is considerable promise in using this approach as a potential screening tool that is easily deployed, low-cost, and high-throughput for clinical disorders, especially in young children and elderly populations who may be less compliant to traditional evaluation tests.
