Evidence map›Paper›PMID 42489546›Full record

ArticleeLife2026

Functional muscle networks as biomarkers of post-stroke motor impairment and therapeutic responsiveness.

David O'Reilly, Giorgia Pregnolato, Andrea Turolla, Pawel Kiper, Ioannis Delis, Giacomo Severini

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

6 authors.

David O'Reilly *School of Biomedical sciences, University of Leeds, Leeds, United Kingdom.ORCID https://orcid.org/0000-0002-8471-9447
Giorgia Pregnolato *IRCCS San Camillo Hospital, Venice, Italy.
Andrea TurollaLaboratory of Healthcare Innovation Technology, IRCCS San Camillo Hospital, Venice, Italy.ORCID https://orcid.org/0000-0002-1609-8060
Pawel KiperHealthcare Innovation Technology Lab, IRCCS San Camillo Hospital, Venice, Italy.
Ioannis Delis *School of Biomedical sciences, University of Leeds, Leeds, United Kingdom.
Giacomo Severini *Insight Research Ireland Centre for Data Analytics, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-2289-9331

Funding

Biotechnology and Biological Sciences Research Council BB/Y513799/1HORIZON EUROPE Marie Sklodowska-Curie Actions 10.3030/101034252Research Ireland 21/FFP-P/10228
6 · The paper itself

Abstract

Standardised assessment of post-stroke motor impairment and treatment responsiveness remains a major clinical challenge. In this study, we tackle this challenge by applying a novel muscle network analysis framework to human stroke survivors undergoing intensive upper-limb motor training (O'Reilly & Delis, 2024). Our approach revealed distinct patterns of redundant and synergistic muscle interactions, collectively reflecting the diverse biomechanical roles of flexor- and extensor-driven networks. From these patterns, we derived new biomarkers that stratified patients by gross motor impairment severity and therapeutic responsiveness, each associated with unique physiological signatures. Remarkably, we identified a shift from redundancy to synergy in muscle coordination as a hallmark of effective motor recovery-a transformation supported by a more precise quantification of impairment over conventional approaches. These findings offer an in-depth characterisation of post-stroke motor recovery and establish a robust, independent tool for evaluating rehabilitation efficacy. Future research should employ this framework to identify biomarkers of activities- and participation-related functional recovery.

Indexed as

Muscle, SkeletalStrokeStroke RehabilitationAgedBiomarkersFemaleHumansMaleMiddle AgedRecovery of FunctionUpper ExtremityBiomarkersbiomarkercomputational biologyhumanmuscle networknetwork information frameworkredundancyStrokesynergysystems biology

Identifiers

PMID42489546
PMCPMC13395455

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.