ArticleBrain communications2022
Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model.
Article in Brain communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07266662 (Comparative Effects of Sensory Augmentation and Neuromodulation on Enhancing Motor Recovery Among Stroke Survivors), which is not on this map. Cited by 17 papers.
What it found
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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.
Comparative Effects of Sensory Augmentation and Neuromodulation on Enhancing Motor Recovery Among Stroke Survivors
Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Motor imagery-based neurofeedback in older adults: neural signatures and feasibility in a randomized controlled trial targeting age-related cognitive decline.Journal of neuroengineering and rehabilitation · 2026Trial
- Trial
- 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
- Controlling thermoreversibility and hole conductivity in thermoresponsive ionic biogels using phase morphology for neurohaptics.Science advances · 2026Article
- Advancing Stroke Recovery Through Operant Conditioning of Corticomuscular Coherence.Restorative neurology and neuroscience · 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
- Effects of Navigated rTMS on Post-Stroke Upper-Limb Function: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.Brain sciences · 2025Review
- Uncovering attempted movements of the paralyzed upper limb after stroke through EEG and EMG.Journal of neuroengineering and rehabilitation · 2025Article
- Enhancing Heart Rate-Based Estimation of Energy Expenditure and Exercise Intensity in Patients Post Stroke.Bioengineering (Basel, Switzerland) · 2024Article
- Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke.Sensors (Basel, Switzerland) · 2024Article
- An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity.Journal of neural engineering · 2024Article
- Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.Journal of neuroengineering and rehabilitation · 2024Article
- NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies.Journal of neuroengineering and rehabilitation · 2024Article
- Article
- Facilitating limb movement after stroke.Nature medicine · 2023Article
- Editorial: Translating Innovations in Stroke Rehabilitation to Improve Recovery and Quality of Life Across the Globe.Frontiers in neurology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
After a neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity protocols interact with the central nervous system to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different targeted neuroplasticity protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here, we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (i) study the mechanisms and patterns of cortical reorganization after a stroke; and (ii) identify and parameterize a targeted neuroplasticity protocol that improves recovery of extension torque. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal torque recovery. To enhance recovery, we interdigitated standard training with trials in which the network was given feedback only from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on ∼20% of the total trials restored lateralized cortical activation and improved recovery of extension torque. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable the identification and parameterization of the most promising targeted neuroplasticity protocols. By providing initial guidance, computational models could facilitate and accelerate the realization of new therapies that improve motor recovery.
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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.