001019187 001__ 1019187
001019187 005__ 20231213202052.0
001019187 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-05233
001019187 037__ $$aFZJ-2023-05233
001019187 041__ $$aEnglish
001019187 1001_ $$0P:(DE-Juel1)190453$$aBi, Hanwen$$b0$$eCorresponding author
001019187 1112_ $$aeSleep Europe Virtual Congress 2023$$cVirtual$$d2023-10-04 - 2023-10-06$$geSleepEurope23$$wGermany
001019187 245__ $$aAssociations between sleep and cognitive performance using the ENIGMA-Sleep data
001019187 260__ $$c2023
001019187 3367_ $$033$$2EndNote$$aConference Paper
001019187 3367_ $$2DataCite$$aOther
001019187 3367_ $$2BibTeX$$aINPROCEEDINGS
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001019187 3367_ $$2ORCID$$aLECTURE_SPEECH
001019187 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1702477700_490$$xAfter Call
001019187 520__ $$aBackground: 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.
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001019187 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
001019187 7001_ $$0P:(DE-HGF)0$$aBülow, Robin$$b1
001019187 7001_ $$0P:(DE-HGF)0$$aDeantoni, Michele$$b2
001019187 7001_ $$0P:(DE-Juel1)131679$$aElmenhorst, David$$b3
001019187 7001_ $$0P:(DE-HGF)0$$aElmenhorst, Eva-Maria$$b4
001019187 7001_ $$0P:(DE-HGF)0$$aEwert, Ralf$$b5
001019187 7001_ $$0P:(DE-HGF)0$$aFerrarelli, Fabio$$b6
001019187 7001_ $$0P:(DE-HGF)0$$aFrenzel, Stefan$$b7
001019187 7001_ $$0P:(DE-HGF)0$$aGrabe, Hans J$$b8
001019187 7001_ $$0P:(DE-HGF)0$$aHoepel, Sanne J. W.$$b9
001019187 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b10
001019187 7001_ $$0P:(DE-HGF)0$$aJahanshad, Neda$$b11
001019187 7001_ $$0P:(DE-HGF)0$$aKeihani, Ahmadreza$$b12
001019187 7001_ $$0P:(DE-Juel1)180212$$aKüppers, Vincent$$b13
001019187 7001_ $$0P:(DE-HGF)0$$aLuik, Annemarie I.$$b14
001019187 7001_ $$0P:(DE-HGF)0$$aMayeli, Ahmad$$b15
001019187 7001_ $$0P:(DE-HGF)0$$aMortazavi, Nasrin$$b16
001019187 7001_ $$0P:(DE-HGF)0$$aNilsonne, Gustav$$b17
001019187 7001_ $$0P:(DE-HGF)0$$aRupp, Julia S$$b18
001019187 7001_ $$0P:(DE-Juel1)190448$$aSaberi, Amin$$b19
001019187 7001_ $$0P:(DE-HGF)0$$aSchmidt, Christina$$b20
001019187 7001_ $$0P:(DE-HGF)0$$aSpiegelhalder, Kai$$b21
001019187 7001_ $$0P:(DE-HGF)0$$aTamm, Sandra$$b22
001019187 7001_ $$0P:(DE-HGF)0$$aThomopoulos, Sophia I.$$b23
001019187 7001_ $$0P:(DE-HGF)0$$aThompson, Paul M.$$b24
001019187 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie$$b25
001019187 7001_ $$0P:(DE-HGF)0$$aVandewalle, Gilles$$b26
001019187 7001_ $$0P:(DE-HGF)0$$aVölzke, Henry$$b27
001019187 7001_ $$0P:(DE-HGF)0$$aWeihs, Antoine$$b28
001019187 7001_ $$0P:(DE-HGF)0$$aWexler, Joseph$$b29
001019187 7001_ $$0P:(DE-HGF)0$$aWittfeld, Katharina$$b30
001019187 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b31$$ufzj
001019187 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b32
001019187 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b33
001019187 7001_ $$0P:(DE-Juel1)188400$$aTahmasian, Masoud$$b34$$eCorresponding author
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