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@INPROCEEDINGS{Bi:1019187,
      author       = {Bi, Hanwen and Bülow, Robin and Deantoni, Michele and
                      Elmenhorst, David and Elmenhorst, Eva-Maria and Ewert, Ralf
                      and Ferrarelli, Fabio and Frenzel, Stefan and Grabe, Hans J
                      and Hoepel, Sanne J. W. and Hoffstaedter, Felix and
                      Jahanshad, Neda and Keihani, Ahmadreza and Küppers, Vincent
                      and Luik, Annemarie I. and Mayeli, Ahmad and Mortazavi,
                      Nasrin and Nilsonne, Gustav and Rupp, Julia S and Saberi,
                      Amin and Schmidt, Christina and Spiegelhalder, Kai and Tamm,
                      Sandra and Thomopoulos, Sophia I. and Thompson, Paul M. and
                      Valk, Sofie and Vandewalle, Gilles and Völzke, Henry and
                      Weihs, Antoine and Wexler, Joseph and Wittfeld, Katharina
                      and Eickhoff, Simon and Patil, Kaustubh and Raimondo,
                      Federico and Tahmasian, Masoud},
      title        = {{A}ssociations between sleep and cognitive performance
                      using the {ENIGMA}-{S}leep data},
      reportid     = {FZJ-2023-05233},
      year         = {2023},
      abstract     = {Background: Sleep disturbance is considered a potential
                      risk factor for cognitive decline and dementia. Previous
                      large-scale studies using the UK-Biobank data have
                      highlighted a significant, albeit small effect size,
                      non-linear relationship between self-reported sleep duration
                      and cognitive performance. In this study, we performed
                      machine learning (ML) analysis based on both self-reported
                      and objective sleep duration and sleep efficiency using the
                      ENIGMA-Sleep data to predict cognitive scores at the
                      individual level.Methods: A total of 1,040 subjects from two
                      ENIGMA-Sleep collaboration sites (SHIP-TREND, Liege) were
                      included. Sleep measurements were sleep duration and sleep
                      efficiency extracted from Polysomnography (PSG) data and
                      Pittsburgh Sleep Quality Index (PSQI). Cognitive performance
                      was measured using Stroop scores (Liege and SHIP-Trend) and
                      N-back Working Memory Accuracy (Liege). In addition, age,
                      sex, BMI, and depression (Beck DepressionInventory (BDI-II))
                      scores were added to our ML models as input features.
                      Multiple ML models were tested, including polynomial
                      regression, support vector machine (SVM) with linear and rbf
                      kernel, and random forest (RF) using the Julearn python
                      library. ML models’ performance was evaluated using mean
                      absolute error, mean squared error, R2, and holdout
                      correlation.Results: Our preliminary results demonstrated
                      that sleep measurements and demographic data were predictive
                      of the Stroop interference score in the Liege dataset(r2 =
                      0.283 ± 0.231, by RF) and the Stroop reaction time in the
                      SHIP-Trend dataset (r2 = 0.108 ± 0.077, by SVM-rbf) using
                      RF, SVM-linear, and SVM-rbf, outperforming polynomial
                      regression. Age was a significant feature in both datasets
                      with mean feature importance of 0.240 ± 0.0586 (RF) in
                      SHIP-Trend and 0.616 ± 0.218 (SVR-rbf) in Liege. Sleep
                      measurements showed weaker importance in the prediction
                      model. In SHIP-Trend dataset, PSG Sleep Duration was the
                      most impactful sleep measurement (feature importance =
                      0.00185 ± 0.00223 by RF), whereas in Liege dataset, it was
                      Self-report Sleep Efficiency(feature importance = 0.0479 ±
                      0.0477 by SVM-rbf). However, the Liege dataset's sleep
                      measurements and demographic data could not predict N-back
                      working memory accuracy.Conclusions: Based on two
                      independent samples, our findings demonstrate that Stroop
                      interference and reaction time scores can be predicted by
                      subjective and objective sleep measurements and demographic
                      data (mainly age) based on ML models.},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {eSleep Europe Virtual Congress 2023,
                       Virtual (Germany), 4 Oct 2023 - 6 Oct
                       2023},
      subtyp        = {After Call},
      cin          = {INM-7 / INM-2},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-2-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2023-05233},
      url          = {https://juser.fz-juelich.de/record/1019187},
}