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@INPROCEEDINGS{Komeyer:1034783,
      author       = {Komeyer, Vera and Eickhoff, Simon and Kasper, Jan and
                      Grefkes, Christian and Patil, Kaustubh and Raimondo,
                      Federico},
      title        = {{P}redicting individual hand grip strength - a multimodal,
                      confound-free machine learning approach},
      reportid     = {FZJ-2024-07537},
      year         = {2024},
      abstract     = {Hand 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.},
      month         = {Sep},
      date          = {2024-09-05},
      organization  = {Motor Mastery Symposium, Köln
                       (Germany), 5 Sep 2024 - 6 Sep 2024},
      subtyp        = {Invited},
      cin          = {INM-7 / INM-3},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / DFG project G:(GEPRIS)431549029 - SFB 1451:
                      Schlüsselmechanismen normaler und krankheitsbedingt
                      gestörter motorischer Kontrolle (431549029)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(GEPRIS)431549029},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1034783},
}