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@ARTICLE{Raimondo:1034745,
      author       = {Raimondo, Federico and Bi, Hanwen and Komeyer, Vera and
                      Kasper, Jan and Primus, Sabrina and Hoffstaedter, Felix and
                      Mandal, Synchon and Waite, Laura and Winkelmann, Juliane and
                      Oexle, Konrad and Eickhoff, Simon B. and Tahmasian, Masoud
                      and Patil, Kaustubh R.},
      title        = {{C}an we predict sleep health based on brain features? {A}
                      large-scale machine learning study},
      reportid     = {FZJ-2024-07502},
      year         = {2024},
      abstract     = {Objectives Normal sleep is crucial for brain health. Recent
                      studies have reported robust associations between sleep
                      disturbance and various brain structural and functional
                      traits. However, the complex interplay between sleep health
                      and macro-scale brain organization remains inconclusive. In
                      this study, we aimed to uncover the links between brain
                      imaging features and diverse sleep health-related
                      characteristics by means of Machine Learning (ML).Methods We
                      used 28,088 participants from the UK Biobank to calculate
                      4677 structural and functional neuroimaging markers. Then,
                      we employed them to predict self-reported insomnia symptoms,
                      sleep duration, easiness getting up in the morning,
                      chronotype, daily nap, daytime sleepiness, and snoring. We
                      built seven different linear and nonlinear ML models for
                      each sleep health-related characteristic to assess their
                      predictability.Results We performed an extensive ML analysis
                      that involved more than 100,000 hours of computing. We
                      observed relatively low performance in predicting all sleep
                      health-related characteristics (e.g., balanced accuracy
                      ranging between 0.50-0.59). Across all models, the best
                      performance achieved was 0.59, using a Linear SVM to predict
                      easiness getting up in the morning.Conclusions The low
                      capability of multimodal neuroimaging markers in predicting
                      sleep health-related characteristics, even under extensive
                      ML optimization in a large population sample suggests a
                      complex relationship between sleep health and brain
                      organization.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.10.13.618080},
      url          = {https://juser.fz-juelich.de/record/1034745},
}