How rapid-acting antidepressants (RAADs), such as ketamine, induce immediate and sustained improvements in mood in patients with major depressive disorder (MDD) is poorly understood. A core feature of MDD is the prevalence of cognitive processing biases associated with negative affective states, and the alleviation of negative affective biases may be an index of response to drug treatment. Here, we used an affective bias behavioral test in rats, based on an associative learning task, to investigate the effects of RAADs. To generate an affective bias, animals learned to associate two different digging substrates with a food reward in the presence or absence of an affective state manipulation. A choice between the two reward-associated digging substrates was used to quantify the affective bias generated. Acute treatment with the RAADs ketamine, scopolamine, or psilocybin selectively attenuated a negative affective bias in the affective bias test. Low, but not high, doses of ketamine and psilocybin reversed the valence of the negative affective bias 24 hours after RAAD treatment. Only treatment with psilocybin, but not ketamine or scopolamine, led to a positive affective bias that was dependent on new learning and memory formation. The relearning effects of ketamine were dependent on protein synthesis localized to the rat medial prefrontal cortex and could be modulated by cue reactivation, consistent with experience-dependent neural plasticity. These findings suggest a neuropsychological mechanism that may explain both the acute and sustained effects of RAADs, potentially linking their effects on neural plasticity with affective bias modulation in a rodent model.

We quantify cellular- and circuit-resolution neural network dynamics following therapeutically relevant doses of the psychedelic psilocybin. Using chronically implanted Neuropixels probes, we recorded local field potentials (LFP) alongside action potentials from hundreds of neurons spanning infralimbic, prelimbic and cingulate subregions of the medial prefrontal cortex of freely-behaving adult rats. Psilocybin (0.3mg/kg or 1mg/kg i.p.) unmasked 100Hz high frequency oscillations that were most pronounced within the infralimbic cortex, persisted for approximately 1h post-injection and were accompanied by decreased net pyramidal cell firing rates and reduced signal complexity. These acute effects were more prominent during resting behaviour than during a sustained attention task. LFP 1-, 2- and 6-days post-psilocybin showed gradually-emerging increases in beta and low-gamma (20-60Hz) power, specific to the infralimbic cortex. These findings reveal features of psychedelic action not readily detectable in human brain imaging, implicating infralimbic network oscillations as potential biomarkers of psychedelic-induced network plasticity over multi-day timescales. ### Competing Interest Statement At time of conceptualization, investigation and writing, CWT and CTG were employees of Compass Pathways. RJP, RG, SF-W and MWJ declare no competing interests.

Oligodendrocytes continue to differentiate from their precursor cells even in adulthood, a process that can be modulated by neuronal activity and experience. Previous work has indicated that conditional ablation of oligodendrogenesis in adult mice leads to learning and memory deficits in a range of behavioral tasks. The current study replicated and re-evaluated evidence for a role of oligodendrogenesis in motor learning, using a complex running wheel task. Further, we found that ablating oligodendrogenesis alters brain microstructure (ex vivo MRI) and brain activity (in vivo EEG) independent of experience with the task. This suggests a role for adult oligodendrocyte formation in the maintenance of brain function and indicates that task-independent changes due to oligodendrogenesis ablation need to be considered when interpreting learning and memory deficits in this model.

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.

Serotonergic psychedelic drugs, such as psilocin (4-hydroxy-N,N-dimethyltryptamine), profoundly alter the quality of consciousness through mechanisms which are incompletely understood. Growing evidence suggests that a single psychedelic experience can positively impact long-term psychological well-being, with relevance for the treatment of psychiatric disorders, including depression. A prominent factor associated with psychiatric disorders is disturbed sleep, and the sleep-wake cycle is implicated in the homeostatic regulation of neuronal activity and synaptic plasticity. However, it remains largely unknown to what extent psychedelic agents directly affect sleep, in terms of both acute arousal and homeostatic sleep regulation. Here, chronic electrophysiological recordings were obtained in mice to track sleep-wake architecture and cortical activity after psilocin injection. Administration of psilocin led to delayed REM sleep onset and reduced NREM sleep maintenance for up to approximately 3 h after dosing, and the acute EEG response was associated primarily with an enhanced oscillation around 4 Hz. No long-term changes in sleep-wake quantity were found. When combined with sleep deprivation, psilocin did not alter the dynamics of homeostatic sleep rebound during the subsequent recovery period, as reflected in both sleep amount and EEG slow-wave activity. However, psilocin decreased the recovery rate of sleep slow-wave activity following sleep deprivation in the local field potentials of electrodes targeting the medial prefrontal and surrounding cortex. It is concluded that psilocin affects both global vigilance state control and local sleep homeostasis, an effect which may be relevant for its antidepressant efficacy.

Serotonergic psychedelic drugs, such as psilocin (4-hydroxy-N,N-dimethyltryptamine), profoundly alter the quality of consciousness through mechanisms which are incompletely understood. Growing evidence suggests that a single psychedelic experience can positively impact long-term psychological well-being, with relevance for the treatment of psychiatric disorders, including depression. A prominent factor associated with psychiatric disorders is disturbed sleep, and the sleep-wake cycle is implicated in the regulation of neuronal firing and activity homeostasis. It remains unknown to what extent psychedelic agents directly affect sleep, in terms of both acute arousal and homeostatic sleep regulation. Here, chronic in vivo electrophysiological recordings were obtained in mice to track sleep-wake architecture and cortical activity after psilocin injection. Administration of psilocin led to delayed REM sleep onset and reduced NREM sleep maintenance for up to approximately 3 hours after dosing, and the acute EEG response was associated primarily with an enhanced oscillation around 4 Hz. No long-term changes in sleep-wake quantity were found. When combined with sleep deprivation, psilocin did not alter the dynamics of homeostatic sleep rebound during the subsequent recovery period, as reflected in both sleep amount and EEG slow wave activity. However, psilocin decreased the recovery rate of sleep slow wave activity following sleep deprivation in the local field potentials of electrodes targeting medial prefrontal and surrounding cortex. It is concluded that psilocin affects both global vigilance state control and local sleep homeostasis, an effect which may be relevant for its antidepressant efficacy.

Manual sleep stage annotation is a time-consuming but often essential step in the analysis of sleep data. To address this bottleneck several algorithms have been proposed that automate this process, reporting performance levels that are on par with manual annotation according to measures of inter-rater agreement. Here we first demonstrate that inter-rater agreement can provide a biased and imprecise measure of annotation quality. We therefore develop a principled framework for assessing performance against a consensus annotation derived from multiple experienced sleep researchers. We then construct a new sleep stage classifier that combines automated feature extraction using linear discriminant analysis with inference based on vigilance state-dependent contextual information using a hidden Markov model. This produces automated annotation accuracies that exceed expert performance on rodent electrophysiological data. Furthermore, our classifier is shown to be robust to errors in the training data, robust to experimental manipulations, and compatible with different recording configurations. Finally, we demonstrate that the classifier identifies both successful and failed attempts to transition between vigilance states, which may offer new insights into the occurrence of short awake periods between REM and NREM sleep. We call our classifier ‘Somnotate’ and make an implementation available to the neuroscience community.

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.

The homeostatic regulation of sleep manifests as a relative constancy of its total daily amount, and the compensation of sleep loss by an increase in its subsequent duration and intensity. Theoretical descriptions of this phenomenon define “Process S”, a variable with dynamics dependent only on sleep-wake history and whose levels are reflected in EEG slow wave activity. While numerous hypotheses have been advanced regarding the substrate and role of Process S, such as synaptic or energy homeostasis, it remains unclear whether these dynamics are fundamentally driven by a need to homeostatically regulate specific variables, or by an unknown innate process which enforces that a certain daily sleep quota is obtained. Sleep is typically defined based on brain-derived criteria, such as behaviour or EEG power spectra, and variation in brain activity during wakefulness has been linked to variation in Process S accumulation. We therefore hypothesised that Process S dynamics might be related to the quantity and characteristics of spiking activity in cortical neurones. Specifically, we assumed that Process S changes as a function of the deviation of neuronal firing rate from a locally defined set point. To relate these dynamics explicitly to patterns of spiking activity, we incorporated the occurrence of network spiking off periods as both the defining measure of Process S and as the determinant of its rate of decay. This approach was able to describe the time course of Process S, crucially without explicit knowledge of the animal’s global sleep-wake state. This work provides a conceptual advance in our understanding of the substrate of sleep homeostasis and provides important links between local and global aspects of sleep regulation.

The homeostatic regulation of sleep manifests as a relative constancy of its total daily amount, and the compensation of sleep loss by an increase in its subsequent duration and intensity. Theoretical descriptions of this phenomenon define "Process S", a variable with dynamics dependent only on sleep-wake history and whose levels are reflected in electroencephalogram (EEG) slow wave activity (0.5 - 4 Hz). While numerous hypotheses have been advanced regarding the substrate and role of Process S, it remains unclear whether it reflects progressive sleep-wake-dependent changes in specific variables, thereby enabling their homeostatic regulation, or whether it instead reflects a time-keeping mechanism which primarily regulates daily sleep-wake quantity. Here we developed novel mathematical models of Process S, assuming that its dynamics are a function of the deviation of neuronal firing rates from a locally defined set point, crucially without explicit knowledge of the animal9s global sleep-wake state. Our results suggest that Process S tracks global sleep-wake history through an integration of local cortical neuronal activity levels over time. We argue that this suggests that the role of sleep homeostasis is to provide a robust intrinsic time-keeping mechanism, which enables individuals to obtain a species-specific and ecologically-relevant quantity of sleep each day, even in the absence of external temporal information.