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J. Benito-Leon is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS39422) , the Recovery, Transformation and Resilience Plan of the Spanish Ministry of Science and Innovation (grant TED2021-130174B-C33, NETremor) , and the Spanish Ministry of Science and Innovation (grant PID2022-138585OB-C33, Resonate) .

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

Publicado en:American Journal Of Medicine. 138 (3): 550-559 - 2025-03-01 138(3), DOI: https://doi.org/10.1016/j.amjmed.2024.04.004

Autores: Benito-Leon, Julian; Lapena, Jose; Garcia-Vasco, Lorena; Cuevas, Constanza; Viloria-Porto, Julie; Calvo-Cordoba, Alberto; Arrieta-Ortubay, Estibaliz; Ruiz-Ruigomez, Maria; Sanchez-Sanchez, Carmen; Garcia-Cena, Cecilia

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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 memoryguided 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. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. center dot The American Journal of Medicine (2025) 138:550-559

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