% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Nazarzadeh:1022104,
      author       = {Nazarzadeh, Kimia and Eickhoff, Simon and Antonopoulos,
                      Georgios and Raimondo, Federico and Hensel, Lukas and
                      Grefkes, Christian and Patil, Kaustubh},
      title        = {{H}andgrip strength prediction using behavioural and
                      anthropometric features in 179{K} individuals from the {UK}
                      {B}iobank},
      school       = {University of Cologne},
      reportid     = {FZJ-2024-01225},
      year         = {2023},
      abstract     = {Handgrip strength (HGS) is an inexpensive, and non-invasive
                      biomarker for assessing motor performance and identifying
                      individuals at risk of motor impairment. Normative models
                      can be used to identify abnormalities and brain changes that
                      affect HGS and other motor functions. The current work
                      examined machine learning-based approaches to predict HGS
                      using a wide range of behavioural phenotypes and
                      anthropometric measures. The data were obtained from healthy
                      controls in the UK Biobank. Participants with dominant
                      handgrip strength < 4 kg were excluded. We included 30
                      behavioural phenotypes, such as cognitive functions,
                      anxiety, depression, and neuroticism. Anthropometric
                      measures included BMI, height, and waist-to-hip
                      circumference ratio. These features were analysed with or
                      without the inclusion of gender as an additional feature, as
                      males and females are known to show HGS differences. Linear
                      SVM and Random Forest were used as predictive models. The
                      performance was evaluated using the R2 score. The
                      cross-validation scheme consisted of 10 repetitions and 5
                      folds. We also examined sample size effects. The study
                      included 2,143 subjects with completed assessments of all
                      behavioural phenotypes. The behavioural features had the
                      lowest R2 score (median = 0.09), while anthropometric
                      features showed a relatively higher R2 score (median =
                      0.49). Adding gender as a feature significantly increased
                      the prediction scores but building the model separately for
                      males and females decreased accuracy. In males, predictions
                      were better than in females with anthropometric features.
                      Furthermore, (non-)dominant HGS could be predicted, but the
                      Left + Right HGS predictions were more accurate. Also, the
                      sample size effects were analysed for anthropometrics from
                      $10\%$ (N=17,957) to $100\%$ (N=179,542). The results
                      demonstrate that the lower sample size of anthropometric
                      features shows a high variance and the performance saturates
                      around N=71k. Gender is a strong confound when modelling
                      HGS. Anthropometric features are better predictors of HGS
                      than behavioural features. Future work involves applying the
                      pipeline to stroke patients to identify brain correlates,
                      such as white matter intensity, and assessing its
                      feasibility in clinical settings. In conclusion, HGS
                      prediction using anthropometric and behavioural features can
                      inform future research in the field of motor performance and
                      have implications for early disease diagnosis and
                      treatment.},
      month         = {Jun},
      date          = {2023-06-12},
      organization  = {Helmholtz AI, Hamburg (Germany), 12
                       Jun 2023 - 14 Jun 2023},
      subtyp        = {After Call},
      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) /
                      5253 - Neuroimaging (POF4-525) / 5252 - Brain Dysfunction
                      and Plasticity (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:(DE-HGF)POF4-5252 / G:(GEPRIS)458640473},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-01225},
      url          = {https://juser.fz-juelich.de/record/1022104},
}