Applying machine learning to dissociate between stroke patients and healthy controls using eye movement features obtained from a virtual reality task.

Conventional neuropsychological tests do not represent the complex and dynamic situations encountered in daily life. Immersive virtual reality simulations can be used to simulate dynamic and interactive situations in a controlled setting. Adding eye tracking to such simulations may provide highly detailed outcome measures, and has great potential for neuropsychological assessment. Here, participants (83 stroke patients and 103 healthy controls) we instructed to find either 3 or 7 items from a shopping list in a virtual super market environment while eye movements were being recorded. Using Logistic Regression and Support Vector Machine models, we aimed to predict the task of the participant and whether they belonged to the stroke or the control group. With a limited number of eye movement features, our models achieved an average Area Under the Curve (AUC) of .76 in predicting whether each participant was assigned a short or long shopping list (3 or 7 items). Identifying participant as either stroke patients and controls led to an AUC of .64. In both classification tasks, the frequency with which aisles were revisited was the most dissociating feature. As such, eye movement data obtained from a virtual reality simulation contain a rich set of signatures for detecting cognitive deficits, opening the door to potential clinical applications.

Unraveling Apathy in Korsakoff Syndrome Patients Receiving Long-Term Care With and Without Cerebrovascular Comorbidity.

BACKGROUND: Korsakoff syndrome (KS) is a severe neuropsychiatric disorder caused by acute deficiency of vitamin B1 and concomitant alcoholism. Patients with KS are particularly vulnerable for cerebrovascular comorbidity. KS is characterized by cognitive and neuropsychiatric symptoms, one of which is apathy. Apathy is a pathological lack of goal-directed behaviors, goal-directed cognitions, and goal-directed emotions. Cerebrovascular accidents are known to carry a risk for developing apathy. Apathy has a dramatic effect on the autonomy and daily lives of patients suffering from this condition. METHODS: We assessed general apathy and related subconstructs in fifteen patients with KS, fifteen patients with KS and cerebrovascular comorbidity who reside in a 24-hour care facility, and fifteen healthy controls. RESULTS: Compared with healthy controls, both KS patient groups showed higher levels of apathy as rated by a close informant. We found no difference between both KS patient groups and the healthy control group on the self-report section of the Pleasant Activities List, suggesting that motivation is still intact in KS patients. It is important to note a discrepancy was found between self-reporting and proxy reporting on this list. KS patients with cerebrovascular comorbidity showed more severe emotional blunting compared to both KS patients without cerebrovascular comorbidity and healthy controls. The competency to consent was lower in patients compared with healthy controls, but no difference was found between KS patients with cerebrovascular comorbidity and those without. CONCLUSIONS: Our results suggest that KS patients show increased levels of general apathy compared with healthy controls. Patients show a diminished competency to consent and increased emotional blunting, while motivation is not compromised. Cerebrovascular comorbidity in KS forms a high risk for emotional blunting. The results of this study suggest that apathy is a severe problem in KS. More attention in both the literature and clinical practice would benefit this complex patient population.