| Title: | A neural signature of sleep deprivation in the human brain |
| Journal: | Nature Communications |
| Published: | 22 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42637707/ |
| DOI: | https://doi.org/10.1038/s41467-026-75661-x |
Publication 21407
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Insufficient sleep disrupts cognitive and emotional functioning, yet the precise neural consequences of sleep loss and their persistence remain unclear. Here, we leverage machine learning and large neuroimaging datasets to identify a candidate neural signature that robustly distinguishes sleep-deprived from well-rested brains. We validate this signature across multiple independent datasets spanning both controlled experimental and real-world settings. The signature not only detects residual neural disturbances following a night of recovery sleep, but also demonstrates sensitivity to partial sleep deprivation. Additionally, it captures natural variations in sleep duration in the general population, independent of experimental manipulation. We further identify distributed connectivity patterns that contribute to the signature, highlighting networks vulnerable to sleep manipulations and those that are resistant or rapidly normalized after recovery sleep. The reliability and generalizability of this neural signature underscore its potential as a biomarker for understanding and monitoring the neural impacts of acute and chronic sleep loss.</p>
13 Keywords
- Adult
- Brain
- Brain Mapping
- Female
- Humans
- Machine Learning
- Magnetic Resonance Imaging
- Male
- Neuroimaging
- Sleep
- Sleep Deprivation
- Sleep Duration
- Young Adult
10 Authors
- Zhenfu Wen
- Edward F. Pace-Schott
- Peter L. Franzen
- Lihan Cui
- Kai Zhang
- Si Gao
- L. Elliot Hong
- Peter Kochunov
- Anne Germain
- Mohammed R. Milad
1 Application
| Application ID | Title |
|---|---|
| 34077 | Building and evaluating multivariate statistical machine learning methods for knowledge extraction from the UK Biobank |
Enabling scientific discoveries that improve human health