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| Hauptseite > Publikationsdatenbank > Can we predict sleep health based on brain features? A large-scale machine learning study using UK Biobank > print |
| Hauptseite > Publikationsdatenbank > Can we predict sleep health based on brain features? A large-scale machine learning study using UK Biobank > print |
| 001 | 1052071 | ||
| 005 | 20260224202422.0 | ||
| 024 | 7 | _ | |a 10.1093/braincomms/fcag016 |2 doi |
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| 100 | 1 | _ | |a Raimondo, Federico |0 P:(DE-Juel1)185083 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Can we predict sleep health based on brain features? A large-scale machine learning study using UK Biobank |
| 260 | _ | _ | |a [Oxford] |c 2026 |b Oxford University Press |
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| 520 | _ | _ | |a 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. |
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| 773 | _ | _ | |a 10.1093/braincomms/fcag016 |g p. fcag016 |0 PERI:(DE-600)3020013-1 |n 1 |p fcag016 |t Brain communications |v 8 |y 2026 |x 2632-1297 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1052071/files/Invoice_SOA26LT000974.pdf |
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