Cognitive computation of brain disorders based primarily on ocular responses

TL;DR

This review explores ocular responses as noninvasive biomarkers for early diagnosis of cognitive and psychiatric disorders, highlighting advances in AI for improved assessment and intervention.

Contribution

It synthesizes current ocular assessment techniques and discusses the integration of AI, especially deep learning, to enhance diagnosis and understanding of brain disorders.

Findings

Ocular responses correlate with cognitive and emotional processing.

Ocular biomarkers can aid early diagnosis of brain disorders.

AI enhances analysis of ocular data for clinical use.

Abstract

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.