Antisaccades and memory-guided saccades in genetic generalized epilepsy and temporal lobe epilepsy

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

BACKGROUND

Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by various brain regions, including the dorsolateral prefrontal cortex and frontal-thalamic circuits, offer a potential metric for evaluating cognitive dysfunction. 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: the latencies, gain, and velocity of 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 in long COVID patients experiencing 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.

Automatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools

This article presents an automatic gaze-tracker system to assist in the detection of minimal hepatic encephalopathy by analyzing eye movements with machine learning tools. To record eye movements, we used video-oculography technology and developed automatic feature-extraction software as well as a machine learning algorithm to assist clinicians in the diagnosis. In order to validate the procedure, we selected a sample (n=47) of cirrhotic patients. Approximately half of them were diagnosed with minimal hepatic encephalopathy (MHE), a common neurological impairment in patients with liver disease. By using the actual gold standard, the Psychometric Hepatic Encephalopathy Score battery, PHES, patients were classified into two groups: cirrhotic patients with MHE and those without MHE. Eye movement tests were carried out on all participants. Using classical statistical concepts, we analyzed the significance of 150 eye movement features, and the most relevant (p-values ≤ 0.05) were selected for training machine learning algorithms. To summarize, while the PHES battery is a time-consuming exploration (between 25-40 min per patient), requiring expert training and not amenable to longitudinal analysis, the automatic video oculography is a simple test that takes between 7 and 10 min per patient and has a sensitivity and a specificity of 93%.

Ocular motility as a measure of cerebral dysfunction in adults with focal epilepsy

OBJECTIVE

Using objective oculomotor measures, we aimed to: (1) compare oculomotor performance in patients with drug-resistant focal epilepsy to healthy controls, and (2) investigate the differential impact of epileptogenic focus laterality and location on oculomotor performance.

METHODS

We recruited 51 adults with drug-resistant focal epilepsy from the Comprehensive Epilepsy Programs of two tertiary hospitals and 31 healthy controls to perform prosaccade and antisaccade tasks. Oculomotor variables of interest were latency, visuospatial accuracy, and antisaccade error rate. Linear mixed models were performed to compare interactions between groups (epilepsy, control) and oculomotor tasks, and between epilepsy subgroups and oculomotor tasks for each oculomotor variable.

RESULTS

Compared to healthy controls, patients with drug-resistant focal epilepsy exhibited longer antisaccade latencies (mean difference = 42.8 ms, P = 0.001), poorer spatial accuracy for both prosaccade (mean difference = 0.4°, P = 0.002), and antisaccade tasks (mean difference = 2.1°, P < 0.001), and more antisaccade errors (mean difference = 12.6%, P < 0.001). In the epilepsy subgroup analysis, left-hemispheric epilepsy patients exhibited longer antisaccade latencies compared to controls (mean difference = 52.2 ms, P = 0.003), while right-hemispheric epilepsy was the most spatially inaccurate compared to controls (mean difference = 2.5°, P = 0.003). The temporal lobe epilepsy subgroup displayed longer antisaccade latencies compared to controls (mean difference = 47.6 ms, P = 0.005).

SIGNIFICANCE

Patients with drug-resistant focal epilepsy exhibit poor inhibitory control as evidenced by a high percentage of antisaccade errors, slower cognitive processing speed, and impaired visuospatial accuracy on oculomotor tasks. Patients with left-hemispheric epilepsy and temporal lobe epilepsy have markedly impaired processing speed. Overall, oculomotor tasks can be a useful tool to objectively quantify cerebral dysfunction in drug-resistant focal epilepsy.