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001034783 037__ $$aFZJ-2024-07537
001034783 1001_ $$0P:(DE-Juel1)187351$$aKomeyer, Vera$$b0$$eCorresponding author
001034783 1112_ $$aMotor Mastery Symposium$$cKöln$$d2024-09-05 - 2024-09-06$$wGermany
001034783 245__ $$aPredicting individual hand grip strength - a multimodal, confound-free machine learning approach
001034783 260__ $$c2024
001034783 3367_ $$033$$2EndNote$$aConference Paper
001034783 3367_ $$2DataCite$$aOther
001034783 3367_ $$2BibTeX$$aINPROCEEDINGS
001034783 3367_ $$2DRIVER$$aconferenceObject
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001034783 520__ $$aHand grip strength (HGS) not only reflects overall strength, but is also closely related to physical disability, cognitive decline and mortality [2,8,5] . Beyond, HGS is a cost-efficient and reliable measure in clinical practice. Despite its ubiquity, the neural mechanisms governing HGS remain unclear. To reveil neural underpinnings driving out-of-sample prediction of HGS, we investigated 9 neuroimaging-derived feature categories in the UK Biobank [3] (N = 22554-33136) in combination with 7 algorithms and ensembles thereof under 5 confound-removal scenarios. Additionally we trained models on sex-split populations to rule out non-linear sex-influences. Only such confound-free models allow for the aimed neural interpretation of predictions. Under the most stringent confounder control, inputting grey matter volume (GMV), fALFF and a collection of 6 white matter microstructural characteristics to the XGBoost algorithm yielded significantly best predictions. Interpretative SHAP analyses reveiled that GMV in the anterior globus pallidus  and microstructural characteristics of sensory input bundles to the thalamus and thalamo-cortical tracts were driving out of sample prediction of HGS. This not only informs us about the single-subject-level neural underpinnings of motor behaviour. Being in line with insights from functional neuroanatomy, our results also bridge a gap between the micro- and macrolevel neuroscientific understanding of motor behaviour.
001034783 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001034783 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001034783 536__ $$0G:(GEPRIS)431549029$$aDFG project G:(GEPRIS)431549029 - SFB 1451: Schlüsselmechanismen normaler und krankheitsbedingt gestörter motorischer Kontrolle (431549029)$$c431549029$$x2
001034783 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b1
001034783 7001_ $$0P:(DE-Juel1)184653$$aKasper, Jan$$b2
001034783 7001_ $$0P:(DE-Juel1)161406$$aGrefkes, Christian$$b3
001034783 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b4
001034783 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b5$$eCorresponding author
001034783 909CO $$ooai:juser.fz-juelich.de:1034783$$pVDB
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001034783 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b1
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001034783 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185083$$aForschungszentrum Jülich$$b5$$kFZJ
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001034783 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
001034783 9141_ $$y2024
001034783 920__ $$lyes
001034783 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001034783 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x1
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