Computational rehabilitation of neglect: Using state-space models to understand the recovery mechanisms.

Abstract

Unilateral spatial neglect is a neuropsychological syndrome often observed in right hemisphere stroke patients. The symptoms differ from subject to subject. A few rehabilitation approaches, e.g. prism adaptation, have demonstrated some effect in reducing the symptoms, but the underlying mechanisms are still largely unclear. Recently, neural models have been proposed to qualitatively describe cortical lesions, the resulting neglect symptoms and the effects of treatment. However, these predictions are qualitative and cannot be used to compare different hypotheses or to interpret symptoms at individual subjects level. Here we propose a computational model of the trial-by-trial dynamics of training-induced recovery from neglect. Neglect is modelled in terms of an impaired internal representation of visual stimuli in the left hemispace. The model assumes that recovery is driven by the mismatch between defective representations of visual stimuli and the corresponding hand positions. The model reproduces the main observations of prism adaptation experiments. Using standard system identification techniques, we fitted the model to data from a rehabilitation trial based on a novel rehabilitation approach based on virtual reality, involving reaching movements within an adaptive environment. Our results suggest that the model can be used to interpret data from individual subjects and to formulate testable hypotheses on the mechanisms of recovery and directions for treatment.