Virtual Reality-based Therapy Research Articles

Neural Reorganization Accompanying Upper Limb Motor Rehabilitation from Stroke with Virtual Reality-Based Gesture Therapy

Background: Gesture Therapy is an upper limb virtual reality rehabilitation-based therapy for stroke survivors. It promotes motor rehabilitation by challenging patients with simple computer games representative of daily activities for self-support. This therapy has demonstrated clinical value, but the underlying functional neural reorganization changes associated with this therapy that are responsible for the behavioral improvements are not yet known. Objective: We sought to quantify the occurrence of neural reorganization strategies that underlie motor improvements as they occur during the practice of Gesture Therapy and to identify those strategies linked to a better prognosis. Methods: Functional magnetic resonance imaging (fMRI) neuroscans were longitudinally collected at 4 time points during Gesture Therapy administration to 8 patients. Behavioral improvements were monitored using the Fugl-Meyer scale and Motricity Index. Activation loci were anatomically labelled and translated to reorganization strategies. Strategies are quantified by counting the number of active clusters in brain regions tied to them. Results: All patients demonstrated significant behavioral improvements (P < .05). Contralesional activation of the unaffected motor cortex, cerebellar recruitment, and compensatory prefrontal cortex activation were the most prominent strategies evoked. A strong and significant correlation between motor dexterity upon commencing therapy and total recruited activity was found (r2 = 0.80; P < .05), and overall brain activity during therapy was inversely related to normalized behavioral improvements (r2 = 0.64; P < .05). Conclusions: Prefrontal cortex and cerebellar activity are the driving forces of the recovery associated with Gesture Therapy. The relation between behavioral and brain changes suggests that those with stronger impairment benefit the most from this paradigm.

Auditory–Visual Aversive Stimuli Modulate the Conscious Experience of Fear

In a natural environment, affective information is perceived via multiple senses, mostly audition and vision. However, the impact of multisensory information on affect remains relatively undiscovered. In this study, we investigated whether the auditory-visual presentation of aversive stimuli influences the experience of fear. We used the advantages of virtual reality to manipulate multisensory presentation and to display potentially fearful dog stimuli embedded in a natural context. We manipulated the affective reactions evoked by the dog stimuli by recruiting two groups of participants: dog-fearful and non-fearful participants. The sensitivity to dog fear was assessed psychometrically by a questionnaire and also at behavioral and subjective levels using a Behavioral Avoidance Test (BAT). Participants navigated in virtual environments, in which they encountered virtual dog stimuli presented through the auditory channel, the visual channel or both. They were asked to report their fear using Subjective Units of Distress. We compared the fear for unimodal (visual or auditory) and bimodal (auditory-visual) dog stimuli. Dog-fearful participants as well as non-fearful participants reported more fear in response to bimodal audiovisual compared to unimodal presentation of dog stimuli. These results suggest that fear is more intense when the affective information is processed via multiple sensory pathways, which might be due to a cross-modal potentiation. Our findings have implications for the field of virtual reality-based therapy of phobias. Therapies could be refined and improved by implicating and manipulating the multisensory presentation of the feared situations.

A multi-front eikonal model of cardiac electrophysiology for interactive simulation of radio-frequency ablation

Virtual reality-based therapy simulation meets a growing interest from the medical community due to its potential impact for the training of medical residents and the planning of therapies. However, computer models of the human anatomy are often very computationally demanding, thus incompatible with the constraints of such interactive simulations.In this paper, we propose a fast model of the cardiac electrophysiology based on an eikonal formulation implemented with an anisotropic fast marching method. We demonstrate the use of this model in the context of a simulator of radio-frequency ablation of cardiac arrhythmia from patient-specific medical imaging data. Indeed, this therapy can be very effective for patients but still suffers from a rather low success rate. Being able to test different ablation strategies on a patient-specific model can have a great clinical impact.In our setting, thanks to a haptic 3D user interface, the user can interactively measure the local extracellular potential, pace locally the myocardium or simulate the burning of cardiac tissue as done in radio-frequency ablation interventions.