Evidence map›Paper›PMID 41507923›Full record

ArticleJournal of neuroengineering and rehabilitation2026

Leveraging neural drive to assess hand motor function in individuals with chronic stroke.

Nicholas Tacca, Jackson T Levine, Mary K Heimann, Bryan R Schlink, Sedona Cady, Samuel C Colachis, Ian Baumgart, Austin Bollinger, Collin Dunlap, Philip Putnam and 5 more

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Nicholas TaccaNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA. tacca@battelle.org.
Jackson T LevineNeurorehabilitation and Neural Engineering Laboratory, Shirley Ryan AbilityLab, Chicago, IL, USA.
Mary K HeimannNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Bryan R SchlinkNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Sedona CadyNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Samuel C ColachisNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Ian BaumgartNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Austin BollingerDepartment of Bioengineering, University of Texas at Dallas, Dallas, TX, USA.
Collin DunlapNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Philip PutnamNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Michael J DarrowNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Lauren WengerdNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
José L PonsNeurorehabilitation and Neural Engineering Laboratory, Shirley Ryan AbilityLab, Chicago, IL, USA.
David A FriedenbergNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.
Eric C MeyersNeurotechnology, Battelle Memorial Institute, Columbus, OH, USA.

Funding

Internal Battelle R&D funds N/A
6 · The paper itself

Abstract

backgroundStroke is a leading cause of disability, with up to 80% of survivors experiencing motor impairments. These impairments are attributed to various factors, including reduced neural drive and altered motor unit firing patterns. Rehabilitation aims to restore motor function by enhancing motor unit recruitment and synchronization. High-density electromyography (HD-EMG) is a valuable tool for evaluating these changes in motor unit activity.

methodsWe tested a wearable HD-EMG forearm sleeve to investigate the relationship between motor function and motor unit properties including firing rate, motor unit module activation, and coherence. Seven individuals with chronic stroke and seven able-bodied individuals attempted 12 cued hand and wrist movements while EMG was recorded. Motor units were decomposed across all movements using convolutive blind source separation.

resultsFewer motor units were detectable in individuals with stroke compared to able-bodied participants. There was a significant reduction in motor unit firing rate during specific movements such as wrist flexion and hand open. Motor unit coupling and activation were altered following stroke, with reduced module activation in 8 of the 12 attempted movements. Furthermore, a reduction in coherence for gross movements and an increase in coherence for more dexterous thumb movements suggest altered neural drive to motor units after stroke that is differentially tuned to the complexity of movement. A combined neural control signature, consisting of multiple motor unit features, demonstrated strong correlation ([Formula: see text]) with clinical motor function scores.

conclusionsThis study demonstrates that HD-EMG can capture detailed motor unit activity and neural control characteristics across multiple forearm muscles in individuals with chronic stroke. By integrating multiple HD-EMG features, this approach provides new insights into neuromuscular alterations linked to hand motor function after stroke. These findings support the use of HD-EMG for monitoring recovery, predicting outcomes, and guiding more targeted rehabilitation, thus advancing both stroke research and patient care.

Indexed as

ElectromyographyHandStrokeAdultAgedChronic DiseaseFemaleHumansMaleMiddle AgedMovementMuscle, SkeletalWearable Electronic DevicesWristBiomarkerElectromyographyMotor unitNeural driveStrokeWearables

Identifiers

PMID41507923
PMCPMC12781281

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.