Evidence map›Paper›PMID 40054446›Full record

ArticleCell2025

Sampling representational plasticity of simple imagined movements across days enables long-term neuroprosthetic control.

Nikhilesh Natraj, Sarah Seko, Reza Abiri, Runfeng Miao, Hongyi Yan, Yasmin Graham, Adelyn Tu-Chan, Edward F Chang, Karunesh Ganguly

Abstract read
In one paragraph

Article in Cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
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.

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

17 citing papers in PubMed.

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  15. Longitudinal study of gesture decoding in a clinical trial participant with ALS.medRxiv : the preprint server for health sciences · 2025
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Nikhilesh NatrajDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; VA San Francisco Healthcare System, San Francisco, CA, USA.
Sarah SekoDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Reza AbiriDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Runfeng MiaoDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Hongyi YanDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Yasmin GrahamDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Adelyn Tu-ChanDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Edward F ChangDepartment of Neurological Surgery, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA.
Karunesh GangulyDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; VA San Francisco Healthcare System, San Francisco, CA, USA. Electronic address: karunesh.ganguly@ucsf.edu.

Funding

Neuroprosthetic Control of an Anthropomorphic Exoskeleton in TetraplegicsDP2HD087955 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GANGULY, KARUNESH · 2015 to 2015
$2.4M
Long-term reliable neuroprosthetic control of a robotic arm and hand using electrocorticography.R01HD111562 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Edward Chang, Karunesh Ganguly · 2024 to 2026
$2.0M
NICHD NIH HHS DP2 HD087955NICHD NIH HHS R01 HD111562
6 · The paper itself

Abstract

The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what the representational stability of simple well-rehearsed actions is, particularly in humans, and their adaptability to new contexts. Using an electrocorticography brain-computer interface (BCI) in tetraplegic participants, we found that the low-dimensional manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. The manifold's absolute location, however, demonstrated constrained day-to-day drift. Strikingly, neural statistics, especially variance, could be flexibly regulated to increase representational distances during BCI control without somatotopic changes. Discernability strengthened with practice and was BCI-specific, demonstrating contextual specificity. Sampling representational plasticity and drift across days subsequently uncovered a meta-representational structure with generalizable decision boundaries for the repertoire; this allowed long-term neuroprosthetic control of a robotic arm and hand for reaching and grasping. Our study offers insights into mesoscale representational statistics that also enable long-term complex neuroprosthetic control.

Indexed as

Brain-Computer InterfacesImaginationNeuronal PlasticityAdultElectrocorticographyFemaleHumansMaleMovementQuadriplegiaRoboticsYoung Adultbrain-computer interfacecontroldeep learningelectrocorticographylearningmanifoldrobot

Identifiers

PMID40054446
PMCPMC11932800

What OpenQuestion holds

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Registered trials

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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.