Exploring Cognitive Dysfunction in Long COVID Patients: Eye Movement Abnormalities and Frontal-Subcortical Circuits Implications via Eye-Tracking and Machine Learning

IF 5.3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL American Journal of Medicine Pub Date : 2024-04-05 DOI:10.1016/j.amjmed.2024.04.004
Julián Benito-León MD, PhD , José Lapeña MD , Lorena García-Vasco MD , Constanza Cuevas PsyD , Julie Viloria-Porto BEng , Alberto Calvo-Córdoba BEng , Estíbaliz Arrieta-Ortubay MD, PhD , María Ruiz-Ruigómez MD, PhD, MD, PhD , Carmen Sánchez-Sánchez MD, PhD , Cecilia García-Cena PhD
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Abstract

Background

Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by several brain areas, such as the dorsolateral prefrontal cortex and frontal-thalamic circuits, provide a potential metric for assessing cortical networks and cognitive status. We aimed to examine the utility of eye movement measurements in identifying cognitive impairments in long COVID patients.

Methods

We recruited 40 long COVID patients experiencing subjective cognitive complaints and 40 healthy controls and used a certified eye-tracking medical device to record saccades and antisaccades. Machine learning was applied to enhance the analysis of eye movement data.

Results

Patients did not differ from the healthy controls regarding age, sex, and years of education. However, the patients' Montreal Cognitive Assessment total score was significantly lower than healthy controls. Most eye movement parameters were significantly worse in patients. These included the latencies, gain (computed as the ratio between stimulus amplitude and gaze amplitude), velocities, and accuracy (evaluated by the presence of hypermetric or hypometria dysmetria) of both visually and memory-guided saccades; the number of correct memory saccades; the latencies and duration of reflexive saccades; and the number of errors in the antisaccade test. Machine learning permitted distinguishing between long COVID patients experiencing subjective cognitive complaints and healthy controls.

Conclusion

Our findings suggest impairments in frontal subcortical circuits among long COVID patients who report subjective cognitive complaints. Eye-tracking, combined with machine learning, offers a novel, efficient way to assess and monitor long COVID patients' cognitive dysfunctions, suggesting its utility in clinical settings for early detection and personalized treatment strategies. Further research is needed to determine the long-term implications of these findings and the reversibility of cognitive dysfunctions.
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探索长期 COVID 患者的认知功能障碍:通过眼动跟踪和机器学习探索长期 COVID 患者的认知功能:眼动异常和额叶-皮层下环路的含义。
认知功能障碍被认为是2019冠状病毒病(COVID-19)后最严重的后遗症之一。眼球运动由几个大脑区域控制,如背外侧前额叶皮层和额丘脑回路,为评估皮层网络和认知状态提供了一个潜在的指标。我们的目的是研究眼动测量在识别长期COVID患者认知障碍方面的效用。方法招募40例有主观认知主诉的长期COVID - 19患者和40例健康对照者,使用经认证的眼动追踪医疗设备记录眼动和反眼动。应用机器学习增强眼动数据分析。结果患者与健康对照组在年龄、性别、受教育年限等方面均无差异。然而,患者的蒙特利尔认知评估总分明显低于健康对照组。大多数患者的眼动参数明显变差。这些包括视觉和记忆引导的扫视的潜伏期、增益(以刺激幅度和凝视幅度之间的比率计算)、速度和准确性(通过存在高或低计量障碍来评估);正确记忆扫视的次数;反身性扫视的潜伏期和持续时间;以及反扫视测试中的错误数。机器学习允许区分经历主观认知抱怨的长期COVID患者和健康对照。结论我们的研究结果表明,在报告主观认知抱怨的长期COVID患者中,额叶皮层下回路存在损伤。眼动追踪与机器学习相结合,为评估和监测长期COVID患者的认知功能障碍提供了一种新颖、有效的方法,表明其在早期发现和个性化治疗策略的临床环境中的实用性。需要进一步的研究来确定这些发现的长期影响和认知功能障碍的可逆性。
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来源期刊
American Journal of Medicine
American Journal of Medicine 医学-医学:内科
CiteScore
6.30
自引率
3.40%
发文量
449
审稿时长
9 days
期刊介绍: The American Journal of Medicine - "The Green Journal" - publishes original clinical research of interest to physicians in internal medicine, both in academia and community-based practice. AJM is the official journal of the Alliance for Academic Internal Medicine, a prestigious group comprising internal medicine department chairs at more than 125 medical schools across the U.S. Each issue carries useful reviews as well as seminal articles of immediate interest to the practicing physician, including peer-reviewed, original scientific studies that have direct clinical significance and position papers on health care issues, medical education, and public policy.
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