Exploring Cognitive Dysfunction in Long COVID Patients: Eye Movement Abnormalities and Frontal-Subcortical Circuits...

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Technology

Background:

• Cognitive dysfunction is a significant long-term effect of COVID-19.
• Eye movements reflect the health of cortical networks and cognitive function.
• Assessing eye movements may reveal cognitive deficits in long COVID patients.

Purpose of the Study:

• To investigate the effectiveness of eye movement measurements in identifying cognitive impairments in long COVID patients.
• To explore the utility of eye-tracking and machine learning for assessing cognitive status after COVID-19.

Main Methods:

• Recruited 40 long COVID patients with cognitive complaints and 40 healthy controls.
• Utilized a certified eye-tracking device to record saccades and antisaccades.
• Applied machine learning algorithms to analyze eye movement data.

Main Results:

• Long COVID patients exhibited significantly lower Montreal Cognitive Assessment scores than controls.
• Impaired eye movement parameters (saccades, antisaccades) were observed in long COVID patients.
• Machine learning successfully differentiated between long COVID patients and healthy controls based on eye movement data.

Conclusions:

• Findings suggest frontal subcortical circuit impairments in long COVID patients with cognitive complaints.
• Eye-tracking combined with machine learning provides an efficient method for assessing long COVID cognitive dysfunction.
• This technology shows promise for early detection and personalized treatment in clinical settings.