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

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Authors: Gilsoon Park, Mahir H. Khan, Justin W. Andrushko, Nerisa Banaj, Michael R Borich, Boyd L.A., Amy Brodtmann, Truman R. Brown, Cathrin M. Buetefisch, Adriana Bastos Conforto, Steven C. Cramer, Michael A. Dimyan
+37 more Martin Domín, Miranda R. Donnelly, Natalia Egorova-Brumley, Elsa R Ermer, Wuwei Feng, Fatemeh Geranmayeh, Colleen A Hanlon, Brenton Hordacre, Neda Jahanshad, Steven A. Kautz, Mohamed Salah Khlif, Jingchun Liu, Martín Lotze, Bradley J MacIntosh, Feroze B. Mohamed, Jan E Nordvik, Fabrizio Piras, Kate Pirog Revill, Andrew D. Robertson, Christian Schranz, Nicolas Schweighofer, Na Jin Seo, Surjo R. Soekadar, Shraddha Srivastava, Bethany P. Tavenner, Gregory Thielman, Sophia I Thomopoulos, Daniela Vecchio, Emilio Werden, Lars T Westlye, Carolee J. Winstein, George F. Wittenberg, Jennifer K. Ferris, Chunshui Yu, Paul M. Thompson, Sook-Lei Liew, Hosung Kim
Cited by: 2
References: 30

Abstract

BACKGROUND: Stroke 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. METHODS: In 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. FINDINGS: We 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

Fields of Study & Subjects

Advanced Neuroimaging Techniques and Applications Transcranial Magnetic Stimulation Studies Spatial Neglect and Hemispheric Dysfunction

Publication details

Journal / source
The Lancet Digital Health
Publisher
Elsevier BV
Volume
8
Issue
1
Pages
100942–100942
Publication date
1 January 2026
Language
EN