Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

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.

Disentangling the role of NAc D1 and D2 cells in hedonic eating

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.

Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice

The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity.

The hypothalamic link between arousal and sleep homeostasis in mice

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.

Neuropeptides as Primary Mediators of Brain Circuit Connectivity

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.

Forward genetics identifies a novel sleep mutant with sleep state inertia and REM sleep deficits

Switches between global sleep and wakefulness states are believed to be dictated by top-down influences arising from subcortical nuclei. Using forward genetics and in vivo electrophysiology, we identified a recessive mouse mutant line characterized by a substantially reduced propensity to transition between wake and sleep states with an especially pronounced deficit in initiating rapid eye movement (REM) sleep episodes. The causative mutation, an Ile102Asn substitution in the synaptic vesicular protein, VAMP2, was associated with morphological synaptic changes and specific behavioral deficits, while in vitro electrophysiological investigations with fluorescence imaging revealed a markedly diminished probability of vesicular release in mutants. Our data show that global shifts in the synaptic efficiency across brain-wide networks leads to an altered probability of vigilance state transitions, possibly as a result of an altered excitability balance within local circuits controlling sleep-wake architecture.

Cortical region-specific sleep homeostasis in mice: effects of time of day and waking experience

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.