0090 Sleep Architecture Associations with Brain Age: A Multi-Site Model Validation Article Swipe
Thomas J. Vanasse
,
Samuel Rusk
,
Yoav Nygate
,
Chris Fernandez
,
Jiaxiao Shi
,
Jessica Arguelles
,
Matthew T Klimper
,
Emerson M. Wickwire
,
Nathaniel F. Watson
,
Dennis Hwang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/sleep/zsad077.0090
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/sleep/zsad077.0090
Introduction Machine Learning (ML) can draw upon complex patient data to predict current and future health status, but the best performing ML models are often difficult to interpret. One such ML-defined health marker is the brain age index (BAI), the difference between ML-predicted age and chronological age (CA) using electroencephalogram (EEG) during sleep. BAI is associated with multiple disease states including increased risk for all-cause mortality. Still, validation of published models has been limited, and the underlying associations between polysomnography sleep architecture metrics and BAI estimation are not well understood. Methods A deep neural network (DNN) model was trained to predict the brain age (BA) of patients using raw EEG signals recorded during clinical polysomnography (PSG). The DNN was trained on N=54,000 PSGs. The model was then validated on two independent datasets: a Kaiser Permanente (KP) Historical PSG Dataset (N=10,694), and a multi-site retrospective PSG dataset (N=15,158). To test association with BAI, ordinary least squares (OLS) regression was performed on these validation datasets using basic features derived from sleep staging architecture and arousal data. Results In the validation datasets, we observed mean absolute error (MAE) of 6.67 (6.69/6.49 F/M, KP dataset) and 5.65 (5.56/5.44 F/M, independent dataset). In the OLS analysis, consistent, negative associations were detected between both N3 and REM sleep percentage vs. BAI. Furthermore, BAI was consistently positively associated with N1 sleep percentage. Each of these effects was significant at p< 0.001. Conclusion This study builds upon and expands prior research by evaluating large multi-site datasets and assessing the relationship of N3/REM sleep duration with the predicted brain age. Using a DNN model, BAI was associated with significantly increased N1 and concomitant decreased N3/REM. Further research is needed to determine if BA is malleable and potentially reduced with clinical intervention and positive changes in lifestyle and wellness-related behavior. Support (if any)
Related Topics
Concepts
Polysomnography
Electroencephalography
Arousal
Sleep (system call)
Logistic regression
Sleep Stages
Regression analysis
Audiology
Ordinary least squares
Regression
Slow-wave sleep
Medicine
Psychology
Artificial intelligence
Statistics
Computer science
Machine learning
Internal medicine
Neuroscience
Mathematics
Psychiatry
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/sleep/zsad077.0090
- https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A40/50467143/zsad077.0090.pdf
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378673681
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378673681Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/sleep/zsad077.0090Digital Object Identifier
- Title
-
0090 Sleep Architecture Associations with Brain Age: A Multi-Site Model ValidationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-01Full publication date if available
- Authors
-
Thomas J. Vanasse, Samuel Rusk, Yoav Nygate, Chris Fernandez, Jiaxiao Shi, Jessica Arguelles, Matthew T Klimper, Emerson M. Wickwire, Nathaniel F. Watson, Dennis HwangList of authors in order
- Landing page
-
https://doi.org/10.1093/sleep/zsad077.0090Publisher landing page
- PDF URL
-
https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A40/50467143/zsad077.0090.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A40/50467143/zsad077.0090.pdfDirect OA link when available
- Concepts
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Polysomnography, Electroencephalography, Arousal, Sleep (system call), Logistic regression, Sleep Stages, Regression analysis, Audiology, Ordinary least squares, Regression, Slow-wave sleep, Medicine, Psychology, Artificial intelligence, Statistics, Computer science, Machine learning, Internal medicine, Neuroscience, Mathematics, Psychiatry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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