Detecting Mild Cognitive Impairment Using Smooth Pursuit and a Modified Corsi Task

Abstract

Over 50 million people today live with some form of dementia as it is the most common neurodegenerative disease in the world. Mild cognitive impairment (MCI) is a stage before dementia symptoms overtly manifest. An estimated 10–15% of patients diagnosed with MCI annually convert to Alzheimer’s dementia. Early detection of MCI is imperative as disease-modifying therapies in development could have the potential to significantly delay disease progression before dementia symptoms develop. There is evidence that observing oculomotor movements during different neuropsychological tasks can serve as a biomarker for MCI. A clinical study with 105 participants was performed at several centres in Ljubljana, Slovenia. All the participants underwent an extensive neurological and psychological evaluation and were, on the basis of this evaluation, divided into two groups: cognitively impaired and healthy controls. At the same time the participants performed several short tasks on the computer screen, including smooth pursuit dot tracking and a modified version of the Corsi block-tapping test. During the tasks, performed using their gaze alone, their eye movements were recorded with an eye-tracker. The eye-tracking data was analysed and a number of features describing the gaze behaviour was proposed. These features were used to construct several machine learning models to predict whether a person exhibits signs of cognitive impairment or not. A model based on random forest classifier achieved the best performance with 80% classification accuracy and an area under the ROC curve of 85%.