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Updated: Jun 26, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
Published on: August 2, 2017
A Hierarchical and Multiscale Framework for Characterizing Mouse Sleep-Wake Dynamics from 14-Day Continuous EEG:
Andrey Kostin1, Anton Saevskiy2, Md Aftab Alam1
1Research Service (151A3), Veterans Affairs Greater Los Angeles Healthcare System, Sepulveda, Los Angeles, CA 91343, USA.
Abstract:
Aging disrupts sleep, but how these changes are structured across circadian time, vigilance states, and sex remains poorly understood, because most prior studies used single-sex cohorts and few days of recordings. We continuously recorded 14 days of EEG/EMG in 24 C57BL/6J mice using a balanced 2 × 2 design (young vs. old; male vs. female; n = 6/group). A comprehensive multiscale analysis of the extended dataset enabled detailed reconstruction of 24 h sleep-wake architecture, better characterization of natural day-to-day variability including across multiple estrous cycles, and detection of rare bouts and transition events. Across seven levels of analysis, from circadian profiles to EEG spectral parameterization, the strongest aging effect was a dark-phase-specific 17-18% loss of theta-dominant active wake (TDW) in both sexes, with reciprocal increases in quiet wake (nTDW) and NREM sleep. We also identified a recurring N-shaped structural motif at the dark-to-light transition, where age-related and several sex-associated differences were most apparent. Broadly, old mice exhibited (i) shorter TDW bouts; (ii) a shift in NREM exit kinetics toward wakefulness; (iii) more brief and poorly consolidated "out-block" NREM episodes; and (iv) a slowing of waking theta and higher low-frequency TDW power. Variance decomposition indicated that statistical power depends more on sample size than on recording length. Together, aging reflects a coordinated, circadian-phase-specific reorganization of sleep-wake architecture. Sex-related and interaction findings should be interpreted as hypothesis-generating pending larger cohorts.

