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Cortical and subcortical circuitry are thought to play distinct roles in the generation of sleep oscillations and global state control, respectively. Here we silenced a subset of neocortical layer 5 pyramidal and archicortical dentate gyrus granule cells in male mice by ablating SNAP25. This markedly increased wakefulness and reduced rebound of electroencephalographic slow-wave activity after sleep deprivation, suggesting a role for the cortex in both vigilance state control and sleep homeostasis. The conventional view is that the cortex generates brain oscillations, while subcortical structures control global sleep–wake switching. This study shows that the cortex plays an important role in both global state control and sleep homeostasis.

Overeating is driven by both the hedonic component (‘liking’) of food, and the motivation (‘wanting’) to eat it. The nucleus accumbens (NAc) is a key brain center implicated in these processes, but how distinct NAc cell populations encode ‘liking’ and ‘wanting’ to shape overconsumption remains unclear. Here, we probed the roles of NAc D1 and D2 cells in these processes using cell-specific recording and optogenetic manipulation in diverse behavioral paradigms that disentangle reward traits of ‘liking’ and ‘wanting’ related to food choice and overeating in healthy mice. Medial NAc shell D2 cells encoded experience-dependent development of ‘liking’, while D1 cells encoded innate ‘liking’ during the first food taste. Optogenetic control confirmed causal links of D1 and D2 cells to these aspects of ‘liking’. In relation to ‘wanting’, D1 and D2 cells encoded and promoted distinct aspects of food approach: D1 cells interpreted food cues while D2 cells also sustained food-visit-length that facilitates consumption. Finally, at the level of food choice, D1, but not D2, cell activity was sufficient to switch food preference, programming subsequent long-lasting overconsumption. By revealing complementary roles of D1 and D2 cells in consumption, these findings assign neural bases to ‘liking’ and ‘wanting’ in a unifying framework of D1 and D2 cell activity.

Evidence from human and animal studies has shown that maternal overnutrition and/or obesity are linked with neurobehavioral changes in the offspring. This fetal programming is characterized by adaptive responses to changes in the nutritional state during early life. In the past decade, an association has been made between overconsumption of highly-palatable food by the mother during fetal development and abnormal behaviors resembling addiction in the offspring. Maternal overnutrition can lead to alterations in the offspring’s brain reward circuitry leading to hyperresponsiveness of this circuit following exposure to calorie-dense foods later in life. Given the accumulating evidence indicating that the central nervous system plays a pivotal role in regulating food intake, energy balance, and the motivation to seek food, a dysfunction in the reward circuitry may contribute to the addiction-like behaviors observed in the offspring. However, the underlying mechanisms leading to these alterations in the reward circuitry during fetal development and their relevance to the increased risk for the offspring to later develop addictive-like behaviors is still unclear. Here, we review the most relevant scientific reports about the impact of food overconsumption during fetal development and its effect on addictive-like behaviors of the offspring in the context of eating disorders and obesity.

Across sleep and wakefulness, brain function requires inter-neuronal interactions lasting beyond seconds. Yet, most studies of neural circuit connectivity focus on millisecond-scale interactions mediated by the classic fast transmitters, GABA and glutamate. In contrast, neural circuit roles of the largest transmitter family in the brain–the slow-acting peptide transmitters–remain relatively overlooked, or described as “modulatory.” Neuropeptides may efficiently implement sustained neural circuit connectivity, since they are not rapidly removed from the extracellular space, and their prolonged action does not require continuous presynaptic firing. From this perspective, we review actions of evolutionarily-conserved neuropeptides made by brain-wide-projecting hypothalamic neurons, focusing on lateral hypothalamus (LH) neuropeptides essential for stable consciousness: the orexins/hypocretins. Action potential-dependent orexin release inside and outside the hypothalamus evokes slow postsynaptic excitation. This excitation does not arise from modulation of classic neurotransmission, but involves direct action of orexins on their specific G-protein coupled receptors (GPCRs) coupled to ion channels. While millisecond-scale, GABA/glutamate connectivity within the LH may not be strong, re-assessing LH microcircuits from the peptidergic viewpoint is consistent with slow local microcircuits. The sustained actions of neuropeptides on neuronal membrane potential may enable core brain functions, such as temporal integration and the creation of lasting permissive signals that act as “eligibility traces” for context-dependent information routing and plasticity. The slowness of neuropeptides has unique advantages for efficient neuronal processing and feedback control of consciousness.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

Background During non-rapid eye movement sleep (NREM), alternating periods of synchronised high (ON period) and low (OFF period) neuronal activity are associated with high amplitude delta band (0.5–4 Hz) oscillations in neocortical electrophysiological signals termed slow waves. As this oscillation is dependent crucially on hyperpolarisation of cortical cells, there is an interest in understanding how neuronal silencing during OFF periods leads to the generation of slow waves and whether this relationship changes between cortical layers. A formal, widely adopted definition of OFF periods is absent, complicating their detection. Here, we grouped segments of high frequency neural activity containing spikes, recorded as multiunit activity from the neocortex of freely behaving mice, on the basis of amplitude and asked whether the population of low amplitude (LA) segments displayed the expected characteristics of OFF periods. Results Average LA segment length was comparable to previous reports for OFF periods but varied considerably, from as short as 8 ms to > 1 s. LA segments were longer and occurred more frequently in NREM but shorter LA segments also occurred in half of rapid eye movement sleep (REM) epochs and occasionally during wakefulness. LA segments in all states were associated with a local field potential (LFP) slow wave that increased in amplitude with LA segment duration. We found that LA segments > 50 ms displayed a homeostatic rebound in incidence following sleep deprivation whereas short LA segments (< 50 ms) did not. The temporal organisation of LA segments was more coherent between channels located at a similar cortical depth. Conclusion We corroborate previous studies showing neural activity signals contain uniquely identifiable periods of low amplitude with distinct characteristics from the surrounding signal known as OFF periods and attribute the new characteristics of vigilance-state-dependent duration and duration-dependent homeostatic response to this phenomenon. This suggests that ON/OFF periods are currently underdefined and that their appearance is less binary than previously considered, instead representing a continuum.

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed \"Somnotate\"—a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

The last decades have shown that maintaining a healthy and balanced diet can support brain integrity and functionality, while an inadequate diet can compromise it. However, still little is known about the effects and utility of so-called healthy snacks or drinks and their immediate short-term effects on cognition and physical performance. Here, we prepared dietary modulators comprising the essential macronutrients at different ratios and a controlled balanced dietary modulator. We assessed, in healthy adult mice, the short-term effects of these modulators when consumed shortly prior to tests with different cognitive and physical demands. A high-fat dietary modulator sustained increased motivation compared to a carbohydrate-rich dietary modulator (p = 0.041) which had a diminishing effect on motivation (p = 0.018). In contrast, a high-carbohydrate modulator had an initial beneficial effect on cognitive flexibility (p = 0.031). No apparent effects of any of the dietary modulators were observed on physical exercise. There is increasing public demand for acute cognitive and motor function enhancers that can improve mental and intellectual performance in daily life, such as in the workplace, studies, or sports activities. Our findings suggest such enhancers should be tailored to the cognitive demand of the task undertaken, as different dietary modulators will have distinct effects when consumed shortly prior to the task.

Sleep and wakefulness are not simple, homogenous all-or-none states but represent a spectrum of substates, distinguished by behavior, levels of arousal, and brain activity at the local and global levels. Until now, the role of the hypothalamic circuitry in sleep–wake control was studied primarily with respect to its contribution to rapid state transitions. In contrast, whether the hypothalamus modulates within-state dynamics (state “quality”) and the functional significance thereof remains unexplored. Here, we show that photoactivation of inhibitory neurons in the lateral preoptic area (LPO) of the hypothalamus of adult male and female laboratory mice does not merely trigger awakening from sleep, but the resulting awake state is also characterized by an activated electroencephalogram (EEG) pattern, suggesting increased levels of arousal. This was associated with a faster build-up of sleep pressure, as reflected in higher EEG slow-wave activity (SWA) during subsequent sleep. In contrast, photoinhibition of inhibitory LPO neurons did not result in changes in vigilance states but was associated with persistently increased EEG SWA during spontaneous sleep. These findings suggest a role of the LPO in regulating arousal levels, which we propose as a key variable shaping the daily architecture of sleep–wake states.

Sleep-wake history, wake behaviors, lighting conditions, and circadian time influence sleep, but neither their relative contribution nor the underlying mechanisms are fully understood. The dynamics of electroencephalogram (EEG) slow-wave activity (SWA) during sleep can be described using the two-process model, whereby the parameters of homeostatic Process S are estimated using empirical EEG SWA (0.5-4 Hz) in nonrapid eye movement sleep (NREMS), and the 24 hr distribution of vigilance states. We hypothesized that the influence of extrinsic factors on sleep homeostasis, such as the time of day or wake behavior, would manifest in systematic deviations between empirical SWA and model predictions. To test this hypothesis, we performed parameter estimation and tested model predictions using NREMS SWA derived from continuous EEG recordings from the frontal and occipital cortex in mice. The animals showed prolonged wake periods, followed by consolidated sleep, both during the dark and light phases, and wakefulness primarily consisted of voluntary wheel running, learning a new motor skill or novel object exploration. Simulated SWA matched empirical levels well across conditions, and neither waking experience nor time of day had a significant influence on the fit between data and simulation. However, we consistently observed that Process S declined during sleep significantly faster in the frontal than in the occipital area of the neocortex. The striking resilience of the model to specific wake behaviors, lighting conditions, and time of day suggests that intrinsic factors underpinning the dynamics of Process S are robust to extrinsic influences, despite their major role in shaping the overall amount and distribution of vigilance states across 24 hr.