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001050728 005__ 20260120203621.0
001050728 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-00471
001050728 037__ $$aFZJ-2026-00471
001050728 1001_ $$0P:(DE-Juel1)187351$$aKomeyer, Vera$$b0$$eCorresponding author
001050728 245__ $$aNeurobiological Predictors of Hand Grip Strength as a Global Health Marker: Methodological Foundations and Interpretable Brain-Behaviour Prediction in Large-Scale Neuroimaging$$f - 2026-01-20
001050728 260__ $$c2026
001050728 300__ $$a151
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001050728 3367_ $$2DRIVER$$adoctoralThesis
001050728 502__ $$aDissertation, HHU Düsseldorf, 2026$$bDissertation$$cHHU Düsseldorf$$d2026
001050728 520__ $$aAging populations worldwide are widening the gap between lifespan and healthspan, underscoring theneed for early, scalable markers of organismal health and intervention targets before overt clinicaldecline. Hand grip strength (HGS) has emerged as a highly reliable and low-cost predictor of systemwidefactors such as frailty, cognitive decline, and mortality. Despite its simplicity and clearmusculoskeletal determinants, explaining its system-wide predictive value requires a deeperunderstanding of underlying brain-level neurobiological architectures, which is currently lacking. Thisthesis addresses this gap by investigating generalizable neural predictors of HGS using machine learning(ML) with large-scale, multi-modal neuroimaging data, grounded in methodological foundations forinterpretable brain-behaviour prediction.A critical review of methodological constraints and potential mitigation strategies inobservational neuroimaging-based ML studies was conducted to promote more reliable andgeneralizable brain-behaviour predictions and interpretations (Study 1). Such studies can be hamperedby pitfalls including data leakage, site-effects in multi-site datasets, misleading post-hoc modelinterpretations arising from feature multicollinearity, and model bias due to confounding. Strict out-ofsampleevaluation and clustering-based interpretation to deal with feature multicollinearity wereidentified as suitable solutions. To support principled confounder selection, a theoretically informed butempirically pragmatic 3-step approach was developped (Study 2). The proposed approach integratesmethdology from causal inference - domain knowledge, directed acyclic graphs, and respective graphrules - with associative data-driven modeling.Building on these foundations, a comprehensive, interpretable multi-modal predictive workflowrevealed generalizable, system-level neuroimaging predictors of HGS in a large, healthy cohort fromthe UK Biobank (Study 3). Across modeling approaches, microstructural integrity – particularly inascending medial lemniscus, thalamic radiations, and associative white-matter pathways – as well assubcortical gray matter volume (GMV), mainly in the anterior globus pallidus, emerged as relevantcontributors. In contrast, cortical structural measures and functional imaging features contributed littleto predictive performance. Collectively, these findings position HGS as a behavioural readout of thebrain’s capacity to coordinate, and integrate information across motor, sensory, cognitive, andmotivational systems, rather than a purely peripheral muscle measure or an isolated motor output.In sum, this thesis establishes a framework for neurobiologically interpretable large-scale brainbehaviourprediction and applies it to elucidate why HGS functions as a powerful marker of globalhealth. By intergating methodological rigor with system-level neuroimaging, it demonstrates howsimple behavioural phenotypes can serve as informative windows into the functioning and integrity ofdistributed neural architectures. Future work should determine whether identified HGS-linked neuralsignatures provide better or earlier prognostic and interventional value than the behavioural measureitself.
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001050728 9141_ $$y2026
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