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.2021 Apr 21:12:584270.
doi: 10.3389/fneur.2021.584270. eCollection 2021.

Computer Vision for Brain Disorders Based Primarily on Ocular Responses

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Computer Vision for Brain Disorders Based Primarily on Ocular Responses

Xiaotao Li et al. Front Neurol..

Abstract

Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and spontaneous 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. In this review, we outline multiple techniques in which ocular assessments may serve as a non-invasive approach for the early detections of various brain disorders, such as autism spectrum disorder (ASD), Alzheimer's disease (AD), schizophrenia (SZ), and major depressive disorder (MDD). In addition, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine learning-based AI, especially computer vision (CV) with deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which is most likely to lead to novel evaluations and interventions for brain disorders. Hence, we highlight the potential of using AI to evaluate brain disorders based primarily on ocular features.

Keywords: brain disorders; cognitive neuroscience; computer vision; eye-brain engineering; ocular assessment; retina.

Copyright © 2021 Li, Fan, Chen, Li, Ning, Lin, Chen, Qin, Yeung, Li, Wang and So.

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Conflict of interest statement

XL and JL were both core members at the start-up company of BIAI INC, USA/BIAI LLC., China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The eye as a window to uncover a healthy level of the brain.(A1) A restful and calm eye (positive state) is shown compared with(A2) a stressful and anxious eye (negative state). Note that a positive state is more frequently associated with an upside view of the eyes, whereas a negative state exhibits a more downside view of eyes. The eye images shown here are presented following permission from the corresponding subjects.(B1) Example of a retinal fundus image in color, whereas(B2, B3) show the same retinal image but in black and white. Machine learning predictions of diabetes and body mass index (BMI) states mainly rely on the features of the vasculature and optic disc, as indicated by the soft attention heat map with green color in those images. The images in(B1–B3) were adapted from Poplin et al. (8) (with permission).(C) Complex neural networks spanning the cortical, subcortical, and cerebellar areas are involved in voluntary saccadic eye movements for attentional control. The image was modified from that of Johnson et al. (11). Red arrows indicate the direct pathway (PEF, the parietal eye fields; FEF, frontal eye field; SEF, supplementary eye field) to the superior colliculus (SC) and brainstem premotor regions, while yellow arrows indicate the indirect pathway to the SC and brainstem premotor regions via the basal ganglia (striatum, subthalamic nucleus, globus pallidus, and substantia nigra pars reticularis).(D) An architectural model of the hierarchy of visual cortical circuitry, modified from Felleman and Van (12). There is a feedforward ascending pathway of the vision system from the retinas to the cortex, as well as a feedback descending pathway from the cortex to multiple downstream areas.(E) A potential application of eye–brain engineering developed to compute human brain states mainly based on smart cameras to detect ocular responses, combined with other biological signals including electroencephalography (EEG) and photoplethysmography (PPG).
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References

    1. Dowling JE. The Retina: An Approachable Part of the Brain. Cambridge: Harvard University Press; (1987).
    1. London A, Benhar I, Schwartz M. The retina as a window to the brain—from eye research to CNS disorders. Nat Rev Neurol. (2013) 9:44. 10.1038/nrneurol.2012.227 - DOI - PubMed
    1. Lopez AD, Murray CC. The global burden of disease, 1990–2020. Nat Med. (1998) 4:1241. 10.1038/3218 - DOI - PubMed
    1. Chiu K, Chan TF, Wu A, Leung IY, So KF, Chang RC. Neurodegeneration of the retina in mouse models of Alzheimer's disease: what can we learn from the retina? Age. (2012) 34:633–49. 10.1007/s11357-011-9260-2 - DOI - PMC - PubMed
    1. Adhikari S, Stark DE. Video-based eye tracking for neuropsychiatric assessment. Ann N Y Acad Sci. (2017) 1387:145–52. 10.1111/nyas.13305 - DOI - PubMed

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