Augmented Visuo-Motor feeback, using Virtual Reality (VR) [and the RGS method], helps stroke patients to re-learn how to use affected limb

Stroke patients with ‘hemiparesis’ have a reduced control of one side of the body and often underuse their affected hand even though they actually still do have motor function in that limb. A protracted period of non-use of the affected ‘paretic’ limb can lead to further loss of function. We can say that the brain with stroke has lost confidence in using the affected limb. This so-called ‘learned non-use’ is a well known phenomenon in stroke patients and has been associated with a reduced quality of life. Of course, using the healthy or less affected limb may immediately improve the ability to realize daily activities. Indeed, some current rehabilitaion therapies adopted to overcome the ‘learned non-use’ effect consist in forcing the patient to use the affected limb by constraining movement of the healthy limb, an intervention called Constraint Induced Movement Therapy (CIMT). However this method has several limitations and the high intensity of its protocols severely compromises its adherence. Thus, there is a need for developing alternative methods to foster the usage of the paretic limb.

Research carried out by members of the Synthetic Perceptive, Emotive and Cognitive Systems group (SPECS) of the UPF Center of Autonomous Systems and Neurorobotics (NRAS) lead by ICREA researcher Paul Verschure has proposed a novel rehabilitation approach called Reinforcement-Induced Movement Therapy (RIMT). In a research paper published in the leading Journal of NeuroEngineering and Rehabilitation, lead author Belén Rubio and colleagues from SPECS and Hospital Joan XXIII from Tarragona, propose to maximize upper limbs use by restoring confidence. This is achieved by showing to the patient that his arm movements are faster and more accurate in VR, thus generating the illusion of a non-impaired motor function The authors hypothesize that through this method we can restore the brain’s confidence in the affected limb, increase the patients self-efficacy, reverse learned non-use, and induce long-term motor improvements.

The authors have conducted a longitudinal clinical study with 18 chronic stroke patients that performed 30 minutes of daily RGS VR-based training during six weeks. The RGS rehabilitation training method combines VR-based training with neuroscience based protocols and automated individualization, and exploits a number of key insights in neuroscience for the optimization of motor recovery. Using RGS, is possible to capture the movements of the user’s upper limbs and map them onto an avatar displayed on a screen in first person perspective, so that the patient can observe the movements of the virtual upper extremities.

During RGS training, the experimental group experienced fast and controlled arm movements in VR. The control group followed the same training protocol but without visual manipulations. Evaluators blinded to group designation performed clinical measurements at the beginning, at the end of the training and at 12-weeks follow-up. Clinical scales were used for the assessment of motor improvements. In order to study and predict the effects of this reinforcement-based intervention the authors implemented a computational model of recovery after stroke.

While both groups showed significant motor gains at 6-weeks post-treatment, only the experimental group continued to exhibit further gains in the motor function of the affected arm at 12-weeks follow-up. This improvement was accompanied by a significant increase in arm-use during training in the experimental group.

Implicitly reinforcing affected limb use by manipulating visuomotor feedback and generating the illusion of non-impairment as proposed by RIMT seems beneficial for inducing significant improvement in chronic stroke patients. By challenging the patients’ self-limiting believe system and perceived low self-efficacy this approach might counteract learned non-use.

This project was supported through ERC project cDAC (FP7-IDEAS-ERC 341196), EC H2020 project socSMCs (H2020-EU.1.2.2. 641321) and MINECO project SANAR (Gobierno de España).

Publication:

Belén Rubio Ballester, Martina Maier, Rosa María San Segundo Mozo, Victoria Castañeda, Armin Duff and Paul F. M. J. Verschure (2016), "Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy ", Published in Journal of NeuroEngineering and Rehabilitation, http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0178-x.