Anton Saevskiy

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.

Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state scoring using single-channel electroencephalogram (EEG) recordings from rats and mice. The algorithm employs artifact processing, multi-band frequency analysis, and Gaussian mixture model (GMM)-based clustering to classify wakefulness, non-rapid, and rapid eye movement sleep (NREM and REM sleep, respectively). Combining narrow and broad frequency bands across the delta, theta, and sigma ranges, it uses a majority voting system to enhance accuracy, with tailored preprocessing and voting criteria improving REM detection. Validation on datasets from 10 rats and 10 mice under standard conditions showed sleep–wake state detection accuracies of 92% and 93%, respectively, closely matching manual scoring and comparable to existing methods. REM sleep detection accuracies of 89% (mice) and 91% (rats) align with previously reported (85–90%). Processing a full day of EEG data within several minutes, the algorithm is advantageous for large-scale and longitudinal studies. Its open-source design, flexibility, and scalability make it a robust, efficient tool for automated rodent sleep scoring, advancing research in standard experimental conditions, including aging and sleep deprivation.

Over the past decades, brain–computer interfaces (BCIs) have been rapidly evolving. A BCI is a system that records brain activity signals using elect

Sleep disruption is a frequent problem of advancing age, often accompanied by low-grade chronic central and peripheral inflammation. We examined whether chronic neuroinflammation in the preoptic and basal forebrain area (POA-BF), a critical sleep–wake regulatory structure, contributes to this disruption. We developed a targeted viral vector designed to overexpress tumor necrosis factor-alpha (TNFα), specifically in astrocytes (AAV5-GFAP-TNFα-mCherry), and injected it into the POA of young mice to induce heightened neuroinflammation within the POA-BF. Compared to the control (treated with AAV5-GFAP-mCherry), mice with astrocytic TNFα overproduction within the POA-BF exhibited signs of increased microglia activation, indicating a heightened local inflammatory milieu. These mice also exhibited aging-like changes in sleep–wake organization and physical performance, including (a) impaired sleep–wake functions characterized by disruptions in sleep and waking during light and dark phases, respectively, and a reduced ability to compensate for sleep loss; (b) dysfunctional VLPO sleep-active neurons, indicated by fewer neurons expressing c-fos after suvorexant-induced sleep; and (c) compromised physical performance as demonstrated by a decline in grip strength. These findings suggest that inflammation-induced dysfunction of sleep- and wake-regulatory mechanisms within the POA-BF may be a critical component of sleep–wake disturbances in aging.

The ventrolateral preoptic area (VLPO) contains GABAergic sleep-active neurons. However, the extent to which these neurons are involved in expressing spontaneous sleep and homeostatic sleep regulatory demands is not fully understood. We used calcium (Ca²⁺) imaging to characterize the activity dynamics of VLPO neurons, especially those expressing the vesicular GABA transporter (VGAT) across spontaneous sleep-waking and in response to homeostatic sleep demands. The VLPOs of wild-type and VGAT-Cre mice were transfected with GCaMP6, and the Ca²⁺ fluorescence of unidentified (UNID) and VGAT cells was recorded during spontaneous sleep-waking and 3 h of sleep deprivation (SD) followed by 1 h of recovery sleep. Although both VGAT and UNID neurons exhibited heterogeneous Ca²⁺ fluorescence across sleep-waking, the majority of VLPO neurons displayed increased activity during nonREM/REM (VGAT, 120/303; UNID, 39/106) and REM sleep (VGAT, 32/303; UNID, 19/106). Compared to the baseline waking, VLPO sleep-active neurons (n = 91) exhibited higher activity with increasing SD that remained elevated during the recovery period. These neurons also exhibited increased Ca²⁺ fluorescence during nonREM sleep, marked by increased slow-wave activity and REM sleep during recovery after SD. These findings support the notion that VLPO sleep-active neurons, including GABAergic neurons, are components of neuronal circuitry that mediate spontaneous sleep and homeostatic responses to sustained wakefulness.