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Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach
Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach
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Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach
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Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach
Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach

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Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach
Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach
Journal Article

Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach

2023
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Overview
Stroke is a debilitating disease affecting millions of people worldwide. Despite the survival rate has significantly increased over the years, many stroke survivors are left with severe impairments impacting their quality of life. Rehabilitation programs have proved to be successful in improving the recovery process. However, a reliable model of sensorimotor recovery and a clear identification of predictive markers of rehabilitation-induced recovery are still needed. This article introduces the cross-modality protocols designed to investigate the rehabilitation treatment's effect in a group of stroke survivors. A total of 75 stroke patients, admitted at the IRCCS San Camillo rehabilitation Hospital in Venice (Italy), will be included in this study. Here, we describe the rehabilitation programs, clinical, neuropsychological, and physiological/imaging [including electroencephalography (EEG), transcranial magnetic stimulation (TMS), and magnetic resonance imaging (MRI) techniques] protocols set up for this study. Blood collection for the characterization of predictive biological biomarkers will also be taken. Measures derived from data acquired will be used as candidate predictors of motor recovery. The integration of cutting-edge physiological and imaging techniques, with clinical and cognitive assessment, dose of rehabilitation and biological variables will provide a unique opportunity to define a predictive model of recovery in stroke patients. Taken together, the data acquired in this project will help to define a model of rehabilitation induced sensorimotor recovery, with the final aim of developing personalized treatments promoting the greatest chance of recovery of the compromised functions.