Machine learning methods detect arm movement impairments in a patient with parieto-occipital lesion using only early kinematic information.

Patients with lesions of the parieto-occipital cortex typically misreach visual targets that they correctly perceive (optic ataxia). Although optic ataxia was described more than 30 years ago, distinguishing this condition from physiological behavior using kinematic data is still far from being an achievement. Here, combining kinematic analysis with machine learning methods, we compared the reaching performance of a patient with bilateral occipitoparietal damage with that of 10 healthy controls. They performed visually guided reaches toward targets located at different depths and directions. Using the horizontal, sagittal, and vertical deviation of the trajectories, we extracted classification accuracy in discriminating the reaching performance of patient from that of controls. Specifically, accurate predictions of the patient's deviations were detected after the 20% of the movement execution in all the spatial positions tested. This classification based on initial trajectory decoding was possible for both directional and depth components of the movement, suggesting the possibility of applying this method to characterize pathological motor behavior in wider frameworks.