Multi-Methodological Characterization of Sleep Deprivation: From Standard EEG Power Spectra to Aperiodic Dynamics in Humans and Mice.
- 1University of Muenster
Abstract
Sleep deprivation is a potent, rapid-acting therapeutic intervention for major depressive disorder, yet its underlying neural mechanisms remain poorly understood, hindering the development of predictive biomarkers. Here, we systematically characterize the electro-physiological signatures of prolonged wakefulness using a multi-methodological approach across three independent datasets in humans and mice. By integrating standard power spectral density analysis with aperiodic component fitting (SpecParam) and highly compar-ative time-series analysis (HCTSA), we identified robust cross-species biomarkers of sleep pressure. Machine learning models revealed that theta power is the most consistent feature for differentiating control and sleep deprivation states, achieving up to 90% classification accuracy. Sleep deprivation significantly increased the spectral offset - suggesting global cortical hyperexcitation - while simultaneously steepening the spectral slope. We interpret this simultaneous shift as a state uncoordinated state of hyperexcited and inefficient neu-ral processing. These findings establish reproducible EEG markers of sleep deprivation that transcend species. Given the clinical utility of wake therapy, we propose that prefrontal the-ta power and spectral offset/slope may serve as mechanism-based predictors of therapeu-tic response. Our results provide a framework for the clinical validation of these biomarkers, potentially enabling personalized chronotherapeutic interventions for psychiatric disorders.
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BibTeX
@misc{kroker2026multimethodological,
title = {{Multi-Methodological Characterization of Sleep Deprivation: From Standard EEG Power Spectra to Aperiodic Dynamics in Humans and Mice.}},
author = {T. Kroker and L. Puder and O. Abbasi and S. Ghiasi and C. Krug and I. Wessing and M. A. Salehinejad and T. Ruland and J. Alferink and U. Dannlowski and P. Ritter and J. Gross},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.750125},
url = {https://www.biorxiv.org/content/10.64898/2026.09.08.750125v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Multi-Methodological Characterization of Sleep Deprivation: From Standard EEG Power Spectra to Aperiodic Dynamics in Humans and Mice.
AU - T. Kroker
AU - L. Puder
AU - O. Abbasi
AU - S. Ghiasi
AU - C. Krug
AU - I. Wessing
AU - M. A. Salehinejad
AU - T. Ruland
AU - J. Alferink
AU - U. Dannlowski
AU - P. Ritter
AU - J. Gross
PY - 2026
DA - 2026/09/08
PB - bioRxiv
DO - 10.64898/2026.09.08.750125
UR - https://www.biorxiv.org/content/10.64898/2026.09.08.750125v1
AB - Sleep deprivation is a potent, rapid-acting therapeutic intervention for major depressive disorder, yet its underlying neural mechanisms remain poorly understood, hindering the development of predictive biomarkers. Here, we systematically characterize the electro-physiological signatures of prolonged wakefulness using a multi-methodological approach across three independent datasets in humans and mice. By integrating standard power spectral density analysis with aperiodic component fitting (SpecParam) and highly compar-ative time-series analysis (HCTSA), we identified robust cross-species biomarkers of sleep pressure. Machine learning models revealed that theta power is the most consistent feature for differentiating control and sleep deprivation states, achieving up to 90% classification accuracy. Sleep deprivation significantly increased the spectral offset - suggesting global cortical hyperexcitation - while simultaneously steepening the spectral slope. We interpret this simultaneous shift as a state uncoordinated state of hyperexcited and inefficient neu-ral processing. These findings establish reproducible EEG markers of sleep deprivation that transcend species. Given the clinical utility of wake therapy, we propose that prefrontal the-ta power and spectral offset/slope may serve as mechanism-based predictors of therapeu-tic response. Our results provide a framework for the clinical validation of these biomarkers, potentially enabling personalized chronotherapeutic interventions for psychiatric disorders.
ER -