ArticleJournal of neuroengineering and rehabilitation2024
Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.
Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Effects of transcranial direct current stimulation combined with gait-oriented motor training on disability, quality of life, and motor function in individuals with subacute stroke: a randomized controlled trial.European journal of physical and rehabilitation medicine · 2025Trial
- Harnessing Neuroplasticity: The Role of Priming in Enhancing Post Stroke Motor Function.Restorative neurology and neuroscience · 2026Review
- Simulated gait training improves joint loading symmetry in unilateral transfemoral bone-anchored limb users.Journal of biomechanics · 2026Article
- Toward precision stroke rehabilitation: an integrated causal machine learning and clinician feedback approach.JAMIA open · 2025Article
- Response to Training in Emotion Recognition Function for Mild TBI/PTSD Survivors: Pilot Study.Brain sciences · 2025Article
- Can Robotic Gait Training with End Effectors Improve Lower-Limb Functions in Patients Affected by Multiple Sclerosis? Results from a Retrospective Case-Control Study.Journal of clinical medicine · 2024Article
- Temporal neural coupling: reconciling the Bobath concept with task-oriented training for post-stroke upper limb recovery-a perspective.Frontiers in neurologyArticle
- Autonomic modulation of neuroplasticity in spinal cord injury rehabilitation: insights from a narrative review.Frontiers in neurologyReview
- Non-linear measures of movement variability in multiple sclerosis: a clinical narrative review of Lyapunov exponent and entropy applications in balance and gait.Frontiers in neurologyReview
- Multimodal Machine Learning to Personalize Transcutaneous Spinal Cord Stimulation for Stroke Rehabilitation.Digital biomarkersArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.
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Registered trials
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