The present review presents multiple techniques in which ocular assessments may serve as a noninvasive approach for the early diagnoses of various cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ), and major depressive disorder (MDD). Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. Additionally, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine-learning-based AI, especially deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which may lead to novel evaluations and interventions via ocular approaches for cognitive and psychiatric disorders.
@article{arxiv.1902.08357,
title = {Cognitive computation of brain disorders based primarily on ocular responses},
author = {Xiaotao Li and Xuejing Chen and Fangfang Fan and Li Ning and Kangguang Lin and Zan Chen and Zhenyun Qin and Albert S. Yeung and Liping Wang and Xiaojian Li and Kwok-Fai So},
journal= {arXiv preprint arXiv:1902.08357},
year = {2020}
}