Pereira Marta Luísa Gonçalves de Freitas, Camargo Marina von Zuben de Arruda, Bellan Ariella Fornachari Ribeiro, Tahira Ana Carolina, Dos Santos Bernardo, Dos Santos Jéssica, Machado-Lima Ariane, Nunes Fátima L S, Forlenza Orestes Vicente
Laboratório de Neurociências (LIM-27), Departamento e Instituto de Psiquiatria HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
LIM-23, Departamento e Instituto de Psiquiatria HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
J Alzheimers Dis. 2020;75(1):261-275. doi: 10.3233/JAD-190690.
Visual search abilities are essential to everyday life activities and are known to be affected in Alzheimer's disease (AD). However, little is known about visual search efficiency in mild cognitive impairment (MCI), a transitive state between normal aging and dementia. Eye movement studies and machine learning methods have been recently used to detect oculomotor impairments in individuals with dementia.
The aim of the present study is to investigate the association between eye movement metrics and visual search impairment in MCI and AD.
127 participants were tested: 43 healthy controls, 51 with MCI, and 33 with AD. They completed an eyetracking visual search task where they had to find a previously seen target stimulus among distractors.
Both patient groups made more fixations on the screen when searching for a target, with longer duration than controls. MCI and AD fixated the distractors more often and for a longer period of time than the target. Healthy controls were quicker and made less fixations when scanning the stimuli for the first time. Machine-learning methods were able to distinguish between controls and AD subjects and to identify MCI subjects with a similar oculomotor profile to AD with a good accuracy.
Results showed that eye movement metrics are useful for identifying visual search impairments in MCI and AD, with possible implications in the early identification of individuals with high-risk of developing AD.
视觉搜索能力对日常生活活动至关重要，且已知在阿尔茨海默病（AD）中会受到影响。然而，对于轻度认知障碍（MCI）这一正常衰老与痴呆之间的过渡状态下的视觉搜索效率，人们了解甚少。眼动研究和机器学习方法最近已被用于检测痴呆患者的动眼神经损伤。
本研究旨在探讨眼动指标与MCI和AD中视觉搜索障碍之间的关联。
对127名参与者进行了测试：43名健康对照者、51名MCI患者和33名AD患者。他们完成了一项眼动追踪视觉搜索任务，即在干扰物中寻找先前看到的目标刺激。
两个患者组在搜索目标时在屏幕上的注视次数更多，持续时间比对照组更长。MCI和AD患者比目标更频繁、更长时间地注视干扰物。健康对照者在首次扫描刺激时速度更快，注视次数更少。机器学习方法能够区分对照组和AD患者，并以较高的准确率识别出具有与AD相似动眼神经特征的MCI患者。
结果表明，眼动指标有助于识别MCI和AD中的视觉搜索障碍，可能对早期识别有发展为AD高风险的个体具有重要意义。
