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@ARTICLE{Raimondo:1052071,
      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 and Tahmasian, Masoud and
                      Patil, Kaustubh},
      title        = {{C}an we predict sleep health based on brain features? {A}
                      large-scale machine learning study using {UK} {B}iobank},
      journal      = {Brain communications},
      volume       = {8},
      number       = {1},
      issn         = {2632-1297},
      address      = {[Oxford]},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2026-00741},
      pages        = {fcag016},
      year         = {2026},
      abstract     = {Numerous correlational and group comparison studies have
                      demonstrated robust associations between sleep health and
                      large-scale brain organization. However, individual
                      differences play a critical role in this relationship,
                      highlighting the need for person-specific analyses. In this
                      study, we aimed to explore whether brain imaging features
                      could predict various sleep health-related traits at the
                      individual level using machine learning (ML) techniques.We
                      utilized data from 28,088 participants in the UK Biobank,
                      extracting 4,677 structural and functional neuroimaging
                      markers. These features were then used to predict a range of
                      self-reported sleep characteristics, including insomnia
                      symptoms, sleep duration, ease of waking in the morning,
                      chronotype, napping behavior, daytime sleepiness, and
                      snoring. For each of these seven traits, we trained both
                      linear and nonlinear ML models to evaluate how well brain
                      imaging data could account for individual differences.Our
                      analyses involved extensive computational resources,
                      equivalent to over 200.000 core-hours (equivalent to 25
                      years of compute time). Despite this, the predictive
                      performance of brain features was consistently low across
                      all models, with balanced accuracy scores ranging from 0.50
                      to 0.59. The highest accuracy achieved (0.59) came from a
                      linear model predicting the ease of getting up in the
                      morning. Notably, models using only demographic variables
                      such as age and sex achieved comparable performance,
                      suggesting that these basic characteristics may largely
                      explain the observed variability.These findings indicate
                      that, even when using a large, well-powered sample and
                      advanced ML techniques, multimodal brain imaging features
                      provide limited predictive value for sleep health at the
                      individual level. This low predictability underscores the
                      complexity of the relationship between sleep and brain
                      structure/function. It also suggests that other biological,
                      environmental, or psychological factors—possibly not
                      captured by current imaging modalities—may play a more
                      substantial role in shaping sleep-related behaviors.},
      cin          = {INM-7},
      ddc          = {610},
      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)16},
      doi          = {10.1093/braincomms/fcag016},
      url          = {https://juser.fz-juelich.de/record/1052071},
}