Evidence map›Paper›PMID 41577565›Full record

Observational studyThe Lancet. Digital health2026

Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study.

Gilsoon Park, Mahir H Khan, Justin W Andrushko, Nerisa Banaj, Michael R Borich, Lara A Boyd, Amy Brodtmann, Truman R Brown, Cathrin M Buetefisch, Adriana B Conforto and 39 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in The Lancet. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
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

49 authors.

Gilsoon ParkMark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
Mahir H KhanChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
Justin W AndrushkoDepartment of Physical Therapy, University of British Columbia, Vancouver, BC, Canada; Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK.
Nerisa BanajDepartment of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
Michael R BorichRehabilitation Medicine and Physical Therapy, Emory University, Atlanta, GA, USA.
Lara A BoydDepartment of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.
Amy BrodtmannCognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, VIC, Australia.
Truman R BrownDepartment of Radiology, Medical University of South Carolina, Mount Pleasant, SC, USA.
Cathrin M BuetefischDepartment of Neurology, Emory University, Atlanta, GA, USA.
Adriana B ConfortoLIM-44, Laboratory of Magnetic Resonance Imaging in Neuroradiology, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil; Hospital Israelita Albert Einstein, Sao Paulo, Brazil.
Steven C CramerDepartment of Neurology, UCLA, Los Angeles, CA, USA; California Rehabilitation Institute, Los Angeles, CA, USA.
Michael DimyanUM Rehabilitation and Orthopaedic Institute, University of Maryland, Baltimore, MD, USA.
Martin DominCore Unit Functional Imaging, University Medicine Greifswald, Greifswald, Germany.
Miranda R DonnellyChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Natalia Egorova-BrumleyMelbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia.
Elsa R ErmerDepartment of Neurology, University of Maryland, Baltimore, MD, USA.
Wuwei FengDepartment of Neurology, Duke University School of Medicine, Durham, NC, USA.
Fatemeh GeranmayehDepartment of Brain Sciences, Imperial College London, London, UK.
Colleen A HanlonDepartment of Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA.
Brenton HordacreInnovation, Implementation, and Clinical Translation in Health, University of South Australia, Adelaide, SA, Australia.
Neda JahanshadImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Steven A KautzDepartment of Health Sciences and Research.
Mohamed Salah KhlifCognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, VIC, Australia.
Jingchun LiuDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
Martin LotzeCore Unit Functional Imaging, University Medicine Greifswald, Greifswald, Germany.
Bradley J MacIntoshMedical Biophysics, University of Toronto, Toronto, ON, Canada.
Feroze B MohamedJefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA.
Jan E NordvikCatoSenteret Rehabilitation Center, Son, Norway.
Fabrizio PirasDepartment of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
Kate P RevillFacility for Education and Research in Neuroscience, Emory University, Atlanta, GA, USA.
Andrew D RobertsonKinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Christian SchranzDepartment of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA.
Nicolas SchweighoferDivision of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA.
Na Jin SeoDepartment of Health Sciences and Research; Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA.
Surjo R SoekadarClinical Neurotechnology, Charité-University Medicine Berlin, Berlin, Germany.
Shraddha SrivastavaCourage Kenny Rehabilitation Institute, Allina Health, Minneapolis, MN, USA.
Bethany P TavennerDepartment of Psychology, University of California, Riverside, Riverside, CA, USA.
Gregory T ThielmanDepartment of Physical Therapy and Neuroscience, Saint Joseph's University, Philadelphia, PA, USA.
Sophia I ThomopoulosImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Daniela VecchioDepartment of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
Emilio WerdenCognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, VIC, Australia.
Lars T WestlyeDepartment of Psychology, University of Oslo, Oslo, Norway.
Carolee J WinsteinDivision of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA.
George F WittenbergGRECC, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
Jennifer K FerrisGraduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada; Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada.
Chunshui YuMedical Biophysics, University of Toronto, Toronto, ON, Canada.
Paul M ThompsonImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Sook-Lei LiewMark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA. Electronic address: sliew@usc.edu.
Hosung KimMark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA. Electronic address: hosung.kim@loni.usc.edu.

Funding

Vascular Contributions to Dementia and Genetic Risk Factors for Alzheimer's DiseaseP01AG052350 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Daniel A Nation, ARTHUR W TOGA · 2016 to 2026
$28.4M
TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2012 to 2022
$14.1M
TrainingU54EB020406 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2014 to 2018
$12.5M
A Data Science Approach To Personalizing Sensorimotor Training Post-StrokeR56NS126748 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI REMY-NERIS, OLIVIER, SCHWEIGHOFER, NICOLAS · 2022 to 2022
$717k
NIA NIH HHS P01 AG052350NIBIB NIH HHS P41 EB015922NIBIB NIH HHS U54 EB020406NINDS NIH HHS R56 NS126748
6 · The paper itself

Abstract

backgroundStroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age difference (PAD) has emerged as a sensitive biomarker of both sensorimotor and cognitive function after stroke. Our previous study showed a higher global brain PAD associated with poorer motor function after stroke. However, the association between local stroke lesion load, regional brain age, and motor impairment is unclear. This study aimed to investigate the associations between focal lesion damage, regional brain PAD in both hemispheres, and motor outcomes in chronic stroke, and to identify key predictors of motor impairment.

methodsIn this multicohort, retrospective, observational study, we included individuals with chronic unilateral stroke (>180 days post stroke) from the ENIGMA Stroke Recovery Working Group dataset and used individuals from the UK Biobank cohort to train the regional brain age prediction model. Structural T1-weighted MRI scans were used to estimate regional brain PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated on the basis of lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain PADs. Structural equation modelling examined directional relationships among corticospinal tract lesion load, ipsilesional brain PAD, motor outcomes, and contralesional brain PAD.

findingsWe included 501 individuals from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts in eight countries) and 17 791 individuals from the UK Biobank dataset. Larger total lesion size was positively associated with higher ipsilesional regional brain PADs (older brain age) across most regions (β=0·5420 to 0·9458 across significantly correlated regions, false discovery rate [FDR]-corrected p<0·05), and with lower brain PAD in the contralesional ventral attention and language network region (β=-0·3747, 95% CI -0·6961 to -0·0534, FDR-corrected p<0·05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain PADs across both hemispheres. Machine learning models identified corticospinal tract lesion load (adjusted mean difference -0·0905, 95% CI -0·1221 to -0·0589, p<0·0001), salience network lesion load (-0·0632, -0·0906 to -0·0358, p<0·0001), and regional brain PAD in the contralesional frontoparietal network (0·9939, 0·4929 to 1·4950, p=0·0001) as the top three predictors of motor outcomes. Structural equation modelling revealed that higher corticospinal tract lesion load was associated with poorer motor outcomes (β=-0·355, 95% CI -0·446 to -0·267, p<0·0001), which were further linked to younger contralesional brain age (0·204, 0·111 to 0·295, p<0·0001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age.

interpretationOur findings reveal that larger stroke lesions are associated with accelerated ageing in the ipsilesional hemisphere and paradoxically decelerated brain ageing in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age might serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke.

fundingUS National Institutes of Health.

Indexed as

BrainDeep LearningMagnetic Resonance ImagingNeuronal PlasticityStrokeAgedAge FactorsFemaleHumansMaleMiddle AgedRetrospective Studies

Identifiers

PMID41577565
PMCPMC12949600

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LicenceCC BY-NC-ND
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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.