ReviewJournal of neuroengineering and rehabilitation2016
Computational neurorehabilitation: modeling plasticity and learning to predict recovery.
Review in Journal of neuroengineering and rehabilitation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 90 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Arousal Response Tool for Neurological Disease
Assessing and Modeling the Mechanisms of Recovery During Robot Aided Neurorehabilitation of Walking
Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Smoothness metrics for reaching performance after stroke. Part 1: which one to choose?Journal of neuroengineering and rehabilitation · 2021Pooled it
- A survey of human shoulder functional kinematic representations.Medical & biological engineering & computing · 2019Pooled it
- Effects of robot viscous forces on arm movements in chronic stroke survivors: a randomized crossover study.Journal of neuroengineering and rehabilitation · 2020Trial
- Effects of robot therapy on upper body kinematics and arm function in persons post stroke: a pilot randomized controlled trial.Journal of neuroengineering and rehabilitation · 2020Trial
- Dissociating motor learning from recovery in exoskeleton training post-stroke.Journal of neuroengineering and rehabilitation · 2018Trial
- Repetitive reaching training combined with transcranial Random Noise Stimulation in stroke survivors with chronic and severe arm paresis is feasible: a pilot, triple-blind, randomised case series.Journal of neuroengineering and rehabilitation · 2017Trial
- Neurorehabilitation in the 21st Century: New Science, New Strategies, New Expectations.Neurorehabilitation and neural repair · 2026Review
- Robotic Technologies for Hand Recovery After Stroke: A Reflection on Research Paths in Honor of Dr. Steven Wolf.Neurorehabilitation and neural repair · 2026Article
- Beyond the Plateau: Repeated Treatment Opportunities Are Associated with Improved Naming in Chronic Post-Stroke Aphasia.Brain sciences · 2026Article
- To learn or not to learn? Model-based estimation of motor learning in a large, "in the wild", home rehabilitation database.PLOS digital health · 2026Article
- Beyond GLM: Inter-Subject Variability as a Complementary Approach to Detect Longitudinal Changes in Emotion Processing in Multiple Sclerosis.Journal of imaging · 2026Article
- Robot-Assisted Gravity Compensation for Upper Limb Motor Rehabilitation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- Signal-Dependent Planning Noise Reduces Task Interference by Assisting in the Formation of Stable Motor Primitives in a Neural Network Model of Muscle Coordination Learning.Neural computation · 2026Article
- Use-dependent learning biases the initial state but not the learning dynamics of implicit adaptation.Experimental brain research · 2026Article
- Condition-dependent disruption of low-frequency EEG-fMRI coupling reveals delayed hemodynamic timing and motor-network reorganization in chronic stroke.NeuroImage. Clinical · 2026Article
- Self-Organizing Recruitment of Compensatory Cortical Areas Post-Stroke Can Maximize Residual Motor Performance.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- A structural causal model for robot-assisted upper-limb neurorehabilitation.Frontiers in rehabilitation sciences · 2026Article
- Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025Review
- Article
- Limited Evidence to Review-Is There an Association Between Cognition and Upper Extremity Motor Reaction Time in Older Adults?NeuroSci · 2025Review
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.