The ventrolateral preoptic area (VLPO) predominantly contains sleep-active neurons and is involved in sleep regulation. The perifornical-hypothalamic area (PF-HA) is a wake-regulatory region and predominantly contains wake-active neurons. VLPO GABAergic/galaninergic neurons project to the PF-HA. Previously, the specific contribution of VLPO neurons projecting to the PF-HA (VLPO > PF-HA^PRJ) in sleep regulation in rats could not be investigated due to the lack of tools that could selectively target these neurons. We determined the contribution of VLPO > PF-HA^PRJ neurons in sleep regulation by selectively activating them using designer receptors exclusively activated by designer drugs (DREADDs) in wild-type Fischer-344 rats. We used a combination of two viral vectors to retrogradely deliver the Cre-recombinase gene, specifically, in VLPO > PF-HA neurons, and further express hM3Dq in those neurons to selectively activate them for delineating their specific contributions to sleep–wake functions. Compared to the control, in DREADD rats, clozapine-N-oxide (CNO) significantly increased fos-expression, a marker of neuronal activation, in VLPO > PF-HA^PRJ neurons (2% vs. 20%, p < 0.01) during the dark phase. CNO treatment also increased nonREM sleep (27% vs. 40%, p < 0.01) during the first 3 h of the dark phase, when rats are typically awake, and after exposure to the novel environment (55% vs. 65%; p < 0.01), which induces acute arousal during the light phase. These results support a hypothesis that VLPO > PF-HA^PRJ neurons constitute a critical component of the hypothalamic sleep–wake regulatory circuitry and promote sleep by suppressing wake-active PF-HA neurons.

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.

A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.

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.

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.