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@ARTICLE{Olfati:1031203,
      author       = {Olfati, Mahnaz and Samea, Fateme and Faghihroohi, Shahrooz
                      and Balajoo, Somayeh Maleki and Küppers, Vincent and Genon,
                      Sarah and Patil, Kaustubh and Eickhoff, Simon B. and
                      Tahmasian, Masoud},
      title        = {{P}rediction of depressive symptoms severity based on sleep
                      quality, anxiety, and gray matter volume: a generalizable
                      machine learning approach across three datasets},
      journal      = {EBioMedicine},
      volume       = {108},
      issn         = {2352-3964},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-05603},
      pages        = {105313 -},
      year         = {2024},
      abstract     = {Background: Depressive symptoms are rising in the general
                      population, but their associated factors are unclear.
                      Although the link between sleep disturbances and depressive
                      symptoms severity (DSS) is reported, the predictive role of
                      sleep on DSS and the impact of anxiety and the brain on
                      their relationship remained obscure.Methods: Using three
                      population-based datasets (N = 1813), we trained the machine
                      learning models in the primary dataset (N = 1101) to assess
                      the predictive role of sleep quality, anxiety problems, and
                      brain structural (and functional) measurements on DSS, then
                      we tested our models' performance in two independent
                      datasets (N = 378, N = 334) to test the generalizability of
                      our findings. Furthermore, we applied our model to a smaller
                      longitudinal subsample (N = 66). In addition, we performed a
                      mediation analysis to identify the role of anxiety and brain
                      measurements on the sleep quality and DSS
                      association.Findings: Sleep quality could predict individual
                      DSS (r = 0.43, R2 = 0.18, rMSE = 2.73), and adding anxiety,
                      contrary to brain measurements, strengthened its prediction
                      performance (r = 0.67, R2 = 0.45, rMSE = 2.25). Importantly,
                      out-of-cohort validations in other cross-sectional datasets
                      and a longitudinal subsample provided robust similar
                      results. Furthermore, anxiety scores, contrary to brain
                      measurements, mediated the association between sleep quality
                      and DSS.Interpretation: Poor sleep quality could predict DSS
                      at the individual subject level across three datasets.
                      Anxiety scores not only increased the predictive model's
                      performance but also mediated the link between sleep quality
                      and DSS.Funding: The study is supported by Helmholtz Imaging
                      Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche
                      Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the
                      Deutsche Forschungsgemeinschaft (DFG, German Research
                      Foundation)-Project-ID 431549029-SFB 1451, the programme
                      "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an
                      initiative of the Ministry of Culture and Science of the
                      State of Northrhine Westphalia.Keywords: Anxiety; Brain;
                      Depressive symptoms severity; Machine learning; Sleep
                      quality.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {39255547},
      UT           = {WOS:001325507600001},
      doi          = {10.1016/j.ebiom.2024.105313},
      url          = {https://juser.fz-juelich.de/record/1031203},
}