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@ARTICLE{Nazarzadeh:1033997,
      author       = {Nazarzadeh, Kimia and Eickhoff, Simon B. and Antonopoulos,
                      Georgios and Hensel, Lukas and Tscherpel, Caroline and
                      Komeyer, Vera and Raimondo, Federico and Müller, Veronika
                      and Grefkes, Christian and Patil, Kaustubh R.},
      title        = {{M}achine {L}earning-{D}riven {C}orrection of {H}andgrip
                      {S}trength: {A} {N}ovel {B}iomarker for {N}eurological and
                      {H}ealth {O}utcomes in the {UK} {B}iobank},
      reportid     = {FZJ-2024-06830},
      year         = {2024},
      abstract     = {Background: Handgrip strength (HGS) is a significant
                      biomarker for overall health, offering a simple,
                      cost-effective method for assessing muscle function. Lower
                      HGS is linked to higher mortality, functional decline,
                      cognitive impairments, and chronic diseases. Considering the
                      influence of anthropometrics and demographics on HGS, this
                      study aims to develop a corrected HGS score using machine
                      learning (ML) models to enhance its utility in understanding
                      brain health and disease. Methods: Using UK Biobank data,
                      sex-specific ML models were developed to predict HGS based
                      on three anthropometric variables and age. A novel
                      biomarker, ∆HGS, was introduced as the difference between
                      true HGS (i.e., directly measured HGS) and bias-free
                      predicted HGS. The neural basis of true HGS and ∆HGS was
                      investigated by correlating them to regional gray matter
                      volume (GMV). Statistical analyses were performed to test
                      their sensitivity to longitudinal changes in stroke and
                      major depressive disorder (MDD) patients compared to matched
                      healthy controls (HC).Results: HGS could be accurately
                      predicted using anthropometric and demographic features,
                      with linear support vector machine (SVM) demonstrating high
                      accuracy. Compared to true HGS, ∆HGS showed high
                      reassessment reliability and stronger, widespread
                      associations with GMV, especially in motor-related regions.
                      Longitudinal analysis revealed that neither HGS nor ∆HGS
                      effectively differentiated patients from matched HC at post
                      time-point.Conclusion: The proposed ∆HGS score exhibited
                      stronger correlations with GMV compared to true HGS,
                      suggesting it better represents the relationship between
                      muscle strength and brain structure. While not effective in
                      differentiating patients from HC at post time-point, the
                      increase in ∆HGS from pre to post time-points in patient
                      cohorts may indicate improved utility for monitoring disease
                      progression, treatment efficacy, or rehabilitation effects,
                      warranting further longitudinal validation.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5253 - Neuroimaging (POF4-525) / SFB 1451 B05 -
                      Einzelfallvorhersagen der motorischen Fähigkeiten bei
                      Gesunden und Patienten mit motorischen Störungen (B05)
                      (458640473)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5253 /
                      G:(GEPRIS)458640473},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.11.27.24318057},
      url          = {https://juser.fz-juelich.de/record/1033997},
}