PDF(Pubmed)
Abstract：
The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a \"window to the brain.\" With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson\'s, and Alzheimer\'s disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.
摘要：
视网膜和大脑有着相似的神经化学和神经发育起源，视网膜，通常被视为“通往大脑的窗口”。“随着视网膜结构和功能的测量变得更容易在临床人群中获得，使用视网膜发现作为影响中枢神经系统疾病的潜在生物标志物的兴趣越来越大。功能性视网膜生物标志物，比如视网膜电图,在神经系统疾病中表现出希望，尽管存在重叠遗传标记的局限性，临床特征或在某些情况下可能降低其特异性的药物作用。这篇叙述性综述总结了中枢神经系统疾病和相关小鼠模型中的主要功能性视网膜发现，并为解释视觉电生理发现所涉及的主要兴奋性和抑制性视网膜神经递质提供了背景。视网膜神经化学的这些变化可能有助于我们对这些条件的理解基于视网膜电生理测试的发现，如闪光，模式，多焦视网膜电图，和眼电图。信号分析和机器学习算法的未来应用可能会为病理生理学提供新的见解。分类，以及包括自闭症在内的这些临床疾病的进展，注意缺陷/多动障碍，双相情感障碍,精神分裂症,抑郁症,帕金森,和老年痴呆症。视觉电生理学在这一领域的新临床应用可能会导致更早,更准确的诊断和更有针对性的治疗干预措施有利于个体患者和管理这些个体及其家人的临床医生.
