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Adjuvant effect of rehabilitation using virtual reality in patients after ischemic stroke

Grant number: 16/25683-1
Support type:Scholarships in Brazil - Master
Effective date (Start): October 01, 2017
Effective date (End): June 30, 2019
Field of knowledge:Health Sciences - Medicine
Principal Investigator:Li Li Min
Grantee:Alline Fernanda de Barros Camargo
Home Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID

Abstract

Stroke is the leading cause of functional disability in adults. Among the most compromising functional consequences for the post-stroke patient, the muscular weakness of a hemicorp is the most prevalent, affecting upper and lower limb function contralateral to the brain injury. Neuroimaging studies have shown that post-stroke patients with motor symptoms have, in addition to a decrease in motor connectivity, a reduction in the number of functional neural networks. This impairment affects not only the ability of individuals to perform their daily living activities independently, but also their quality of life and behavior. Thus, the recommended treatment for these patients is multidisciplinary and global, seeking their return to functionality and to the maximum of possible independence. Virtual rehabilitation (VR) has shown promising results, as adjuvant of conventional rehabilitation, allowing the realization of natural movement patterns in varied environments and enabling the activation of neural networks. This experimental study intends to evaluate the participation of VR on the neuroplasticity of the neural networks and how these are related to the functional recovery of upper limb and gait in patients with chronic stroke by means of the adjuvant application of VR to conventional physiotherapy during a period of 6 weeks. It is hoped, at the end of this research, to observe the improvement of the patient in functional aspects and also connectivity of neural networks, through the analysis of magnetic resonance imaging.