Motor learning in hemi-Parkinson using VR-manipulated sensory feedback

Modalities for rehabilitation of the neurologically affected upper-limb (UL) are generally of limited benefit. The majority of patients seriously affected by UL paresis remain with severe motor disability, despite all rehabilitation efforts. Consequently, extensive clinical research is dedicated to develop novel strategies aimed to improve the functional outcome of the affected UL. We have developed a novel virtual-reality training tool that exploits the voluntary control of one hand and provides real-time movement-based manipulated sensory feedback as if the other hand is the one that moves. The aim of this study was to expand our previous results, obtained in healthy subjects, to examine the utility of this training setup in the context of neuro-rehabilitation.

We tested the training setup in patient LA, a young man with significant unilateral UL dysfunction stemming from hemi-parkinsonism. LA underwent daily intervention in which he intensively trained the non-affected upper limb, while receiving online sensory feedback that created an illusory perception of control over the affected limb. Neural changes were assessed using functional magnetic resonance imaging (fMRI) scans before and after training.

Training-induced behavioral gains were accompanied by enhanced activation in the pre-frontal cortex and a widespread increase in resting-state functional connectivity.

Our combination of cutting edge technologies, insights gained from basic motor neuroscience in healthy subjects and well-known clinical treatments, hold promise for the pursuit of finding novel and more efficient rehabilitation schemes for patients suffering from hemiplegia.Implications for rehabilitationAssistive devices used in hospitals to support patients with hemiparesis require expensive equipment and trained personnel - constraining the amount of training that a given patient can receive. The setup we describe is simple and can be easily used at home with the assistance of an untrained caregiver/family member. Once installed at the patient's home, the setup is lightweight, mobile, and can be used with minimal maintenance . Building on advances in machine learning, our software can be adapted to personal use at homes. Our findings can be translated into practice with relatively few adjustments, and our experimental design may be used as an important adjuvant to standard clinical care for upper limb hemiparesis.