Evidence map›Paper›PMID 36458210›Full record

ArticleBrain communications2022

Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model.

Sumner L Norman, Jonathan R Wolpaw, David J Reinkensmeyer

Registry-linked trialOpen access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from 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.

2 · The registry

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.

NCT07266662 narecruitingnot on this mapstarted 2025, after this paper: background citation

Comparative Effects of Sensory Augmentation and Neuromodulation on Enhancing Motor Recovery Among Stroke Survivors

TypeinterventionalSponsorLahore University of Biological and Applied SciencesRan2025 to 2026Enrolled36ConditionsStroke (CVA) or Transient Ischemic AttackArmsSensory Augmentation, Neuromodulation, Combination of Sensory Augmentation and Neuromodulation
3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Sumner L NormanBiology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0001-9945-697X
Jonathan R WolpawNational Center for Adaptive Neurotechnologies, Stratton VA Medical Center and State University of New York, Albany, NY 12208, USA.
David J ReinkensmeyerMechanical and Aerospace Engineering, Anatomy and Neurobiology, University of California: Irvine, Irvine, CA 92697, USA.
California Institute of Technology · USUniversity at Albany, State University of New York · USUniversity of California, Irvine · US

Funding

Training and DisseminationP41EB018783 · NIBIB · WADSWORTH CENTER · PI Jonathan Rickel Wolpaw · 2014 to 2026
$15.4M
EFFECTIVENESS OF ROBOT-ASSISTED HAND MOVEMENT TRAINING AFTER STROKER01HD062744 · NICHD · UNIVERSITY OF CALIFORNIA-IRVINE · PI David Jay Reinkensmeyer · 2010 to 2026
$3.5M
NIBIB NIH HHS P41 EB018783NICHD NIH HHS R01 HD062744
6 · The paper itself

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.

Indexed as

activity-dependent plasticitymotor controlrehabilitationreinforcement learningstroke

Identifiers

PMID36458210
PMCPMC9700163
OpenAlexW4306986359

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.