Background: Sensorimotor impairments of the upper limb (UL) are common after stroke, but there is a lack of evidence-based interventions to improve functioning of UL.
Objective: To evaluate (1) the efficacy of sensory relearning and task-specific training compared to task-specific training only, and (2) the feasibility of the training in chronic stroke.
Design: A pilot randomized controlled trial.
Setting: University hospital outpatient clinic.
Participants: Twenty-seven participants (median age; 62 years, 20 men) were randomized to an intervention group (IG; n = 15) or to a control group (CG; n = 12).
Intervention: Both groups received training twice weekly in 2.5-hour sessions for 5 weeks. The training in the IG consisted of sensory relearning, task-specific training, and home training. The training in the CG consisted of task-specific training.
Main Outcome Measures: Primary outcome was sensory function (touch thresholds, touch discrimination, light touch, and proprioception). Secondary outcomes were dexterity, ability to use the hand in daily activities, and perceived participation. A blinded assessor conducted the assessments at baseline (T1), post intervention (T2), and at 3-month follow-up (T3). Nonparametric analyses and effect-size calculations (r) were performed. Feasibility was evaluated by a questionnaire.
Results: After the training, only touch thresholds improved significantly from T1 to T2 (p = .007, r = 0.61) in the IG compared to the CG. Within the IG, significant improvements were found from T1 to T2 regarding use of the hand in daily activities (p = .001, r = 0.96) and movement quality (p = .004, r = 0.85) and from T1 to T3 regarding satisfaction with performance in meaningful activities (p = .004, r = 0.94). The CG significantly improved the performance of using the hand in meaningful activities from T1 to T2 (p = .017, r = 0.86). The training was well tolerated by the participants and performed without any adverse events.
Conclusions: Combined sensory relearning and task-specific training may be a promising and feasible intervention to improve UL sensorimotor function after stroke.
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http://dx.doi.org/10.1002/pmrj.12767 | DOI Listing |
J Neuroeng Rehabil
November 2024
Department of Cognitive Robotics, Delft University of Technology, Delft, 2628, The Netherlands.
Sci Rep
June 2024
Centre for Synaptic Plasticity, School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, BS8 1TD, UK.
Head-fixation of mice enables high-resolution monitoring of neuronal activity coupled with precise control of environmental stimuli. Virtual reality can be used to emulate the visual experience of movement during head fixation, but a low inertia floating real-world environment (mobile homecage, MHC) has the potential to engage more sensory modalities and provide a richer experimental environment for complex behavioral tasks. However, it is not known whether mice react to this adapted environment in a similar manner to real environments, or whether the MHC can be used to implement validated, maze-based behavioral tasks.
View Article and Find Full Text PDFbioRxiv
June 2024
Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
Savings refers to the gain in performance upon relearning a task. In sensorimotor adaptation, savings is tested by having participants adapt to perturbed feedback and, following a washout block during which the system resets to baseline, presenting the same perturbation again. While savings has been observed with these tasks, we have shown that the contribution from implicit sensorimotor adaptation, a process that uses sensory prediction errors to recalibrate the sensorimotor map, is actually attenuated upon relearning (Avraham et al.
View Article and Find Full Text PDFJ Vis
May 2024
Division of Arts and Sciences, NYU Shanghai, Shanghai, China.
Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks.
View Article and Find Full Text PDFF1000Res
April 2024
Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, 442001, India.
Background: Stroke is ischemia and neurological dysfunction caused by acute brain circulation loss. It causes acute localized neurological abnormalities such as weakness, sensory deficit, or language issues that require long-term treatment. These deficiencies harm the patient and their family psychologically, socially, and economically.
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