Feasibility and efficacy of virtual reality rehabilitation compared with conventional physiotherapy for upper extremity impairment due to ischaemic stroke: protocol for a randomised controlled trial.

INTRODUCTION: Approximately half of all stroke survivors have persistent upper extremity functional impairment, leading to reduced self-care, independence and quality of life. High-intensity, task-oriented virtual reality rehabilitation improves motor recovery. However, its clinical efficacy over standard rehabilitation remains uncertain. This study aims to evaluate the feasibility and efficacy of a virtual reality-based comprehensive rehabilitation gaming system (VR-cRGS) in stroke survivors with upper extremity impairment and to characterise the structural and functional plasticity of the affected regions in the brain due to the proposed rehabilitation. METHODS AND ANALYSIS: This study is a multicentric, open-label, randomised controlled trial with an intention-to-treat analysis. A total of 162 patients will be enrolled in two academic institutes in India that specialise in stroke care. Patients with a first-ever ischaemic stroke (18-70 years and 1-6 months of stroke onset) with upper extremity impairment with 1 and 1+ grades of spasticity as per the modified Ashworth Scale and 3, 4 or 5 stages on Brunnstrom recovery staging will be enrolled. They will be randomised (1:1) into two treatment groups to receive 12 weeks of training either on VR-cRGS or on conventional physiotherapy. The primary feasibility outcome is compliance with the treatment. The primary efficacy outcome is the functional recovery of the upper extremity assessed by the Fugl-Meyer Assessment-Upper Extremity and Wolf Motor Function Test. The secondary outcomes are the Barthel Index and the 36-item Short-Form Health Survey. Multimodal brain imaging will be done in all enrolled patients at baseline and post-treatment to evaluate the structural and functional connectivity changes. The outcome measures will be analysed using paired t-tests or non-parametric tests. ETHICS AND DISSEMINATION: The study has been approved by the Institutional Ethics Review Board of the Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India (SCT/IEC/1415/AUGUST-2019) and the National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India (NIMHANS/IEC (BS and NS DIV.)/32nd Meeting/21). All participants will sign an informed consent form prior to participation. The study results will be disseminated through scholarly publication. TRIAL REGISTRATION NUMBER: CTRI/2021/11/038339.

Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain.

A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function.