Consolidation of initially encoded hippocampal representations in the neocortex through reactivation is crucial for long-term memory formation and is facilitated by the coordination of hippocampal sharp-wave ripples (SWRs) with cortical slow and spindle oscillations during non-REM sleep. Recent evidence suggests that high-frequency cortical ripples can also coordinate with hippocampal SWRs in support of consolidation; however, the contribution of cortical ripples to reactivation remains unclear. We used high-density, continuous recordings in the hippocampus (area CA1) and prefrontal cortex (PFC) over the course of spatial learning and show that independent PFC ripples dissociated from SWRs are prevalent in NREM sleep and predominantly suppress hippocampal activity. PFC ripples paradoxically mediate top-down suppression of hippocampal reactivation rather than coordination, and this suppression is stronger for assemblies that are reactivated during coordinated CA1-PFC ripples for consolidation of recent experiences. Further, we show non-canonical, serial coordination of independent cortical ripples with slow and spindle oscillations, which are known signatures of memory consolidation. These results establish a role for prefrontal cortical ripples in top-down regulation of behaviorally relevant hippocampal representations during consolidation.

OBJECTIVE: Besides the quantification of orexin-A/hypocretin-1 cerebrospinal fluid (CSF) levels in narcolepsy for diagnostic purposes, several other CSF biomarkers have been evaluated, although with controversial results. Since CSF lactate concentrations fluctuate according to the sleep-wake cycle with higher levels during wakefulness and lower levels during sleep, as documented in animal model studies, the present study aimed at quantifying the CSF lactate levels in patients with narcolepsy type 1 (NT1) and 2 (NT2), which are two sleep disorders featured by excessive daytime sleepiness (EDS).
METHODS: Patients with NT1 and NT2 were enrolled in this study and compared to a control group of similar age and sex. All the subjects included in the study underwent a polysomnographic study followed by lumbar puncture for the quantification of CSF lactate levels at awakening.
RESULTS: 23 NT1 (43.5 % male; 36.43 ± 11.89 years) and 15 NT2 patients (46.7 % male; 37.8 ± 14.1 years) were compared to 17 controls (58.8 % male; 32.3 ± 8.4 years). CSF lactate concentrations were reduced in patients with NT1 and NT2 compared to controls but no differences were found between the two groups of patients. ROC curves analysis showed that CSF lactate ≤1.3 mmol/l had a sensitivity of 96.49 and a specificity of 82.35 % for discriminating patients with narcolepsy from controls.
CONCLUSIONS: The present study showed a decrease in CSF lactate levels in patients with narcolepsy. Notably, the reduction of lactate levels was present in both NT1 and NT2 patients, independently of CSF orexin levels. Narcolepsy patients present EDS with daytime napping and REM-related episodes, possibly substantiating the CSF lactate levels reduction related to the impaired daytime wakefulness which was demonstrated in animal studies. Moreover, CSF lactate levels present a good sensitivity and adequate specificity for differentiating narcolepsy from controls. Further studies are needed to understand the role of CSF lactate and its usefulness for monitoring daytime vigilance in patients with narcolepsy.

The shape of the cranium is one of the most notable physical changes induced in domestic dogs through selective breeding and is measured using the cephalic index (CI). High CI (a ratio of skull width to skull length > 60) is characterized by a short muzzle and flat face and is referred to as brachycephaly. Brachycephalic dogs display some potentially harmful changes in neuroanatomy, and there are implications for differences in behavior, as well. The path from anatomy to cognition, however, has not been charted in its entirety. Here, we report that sleep-physiological markers of white-matter loss (high delta power, low frontal spindle frequency, i.e., spindle waves/s), along with a spectral profile for REM (low beta, high delta) associated with low intelligence in humans, are each linked to higher CI values in the dog. Additionally, brachycephalic subjects spent more time sleeping, suggesting that the sleep apnea these breeds usually suffer from increases daytime sleepiness. Within sleep, more time was spent in the REM sleep stage than in non-REM, while REM duration was correlated positively with the number of REM episodes across dogs. It is currently not clear if the patterns of sleep and sleep-stage duration are mainly caused by sleep-impairing troubles in breathing and thermoregulation, present a juvenile-like sleeping profile, or are caused by neuro-psychological conditions secondary to the effects of brachycephaly, e.g., frequent REM episodes are known to appear in human patients with depression. While future studies should more directly address the interplay of anatomy, physiology, and behavior within a single experiment, this represents the first description of how the dynamics of the canine brain covary with CI, as measured in resting companion dogs using a non-invasive sleep EEG methodology. The observations suggest that the neuroanatomical changes accompanying brachycephaly alter neural systems in a way that can be captured in the sleep EEG, thus supporting the utility of the latter in the study of canine brain health and function.

Sleep and wake are defined through physiological and behavioral criteria and can be typically separated into non-rapid eye movement (NREM) sleep stages N1, N2, and N3, rapid eye movement (REM) sleep, and wake. Sleep and wake states are not homogenous in time. Their properties vary during the night and day cycle. Given that brain activity changes as a function of NREM, REM, and wake during the night and day cycle, are seizures more likely to occur during NREM, REM, or wake at a specific time? More generally, what is the relationship between sleep-wake cycles and epilepsy? We will review specific examples from clinical data and results from experimental models, focusing on the diversity and heterogeneity of these relationships. We will use a top-down approach, starting with the general architecture of sleep, followed by oscillatory activities, and ending with ionic correlates selected for illustrative purposes, with respect to seizures and interictal spikes. The picture that emerges is that of complexity; sleep disruption and pathological epileptic activities emerge from reorganized circuits. That different circuit alterations can occur across patients and models may explain why sleep alterations and the timing of seizures during the sleep-wake cycle are patient-specific.

The evolutionary origins of sleep and its sub-states, rapid eye movement (REM) and non-REM (NREM) sleep, found in mammals and birds, remain a mystery. Although the discovery of a single type of sleep in jellyfish suggests that sleep evolved much earlier than previously thought, it is unclear when and why sleep diversified into multiple types of sleep. Intriguingly, multiple types of sleep have recently been found in animals ranging from non-avian reptiles to arthropods to cephalopods. Although there are similarities between these states and those found in mammals and birds, notable differences also exist. The diversity in the way sleep is expressed confounds attempts to trace the evolution of sleep states, but also serves as a rich resource for exploring the functions of sleep.

Circuit-level communication between disparate brain regions is fundamental for the complexities of the central nervous system operation. Co-ordinated bouts of rhythmic activity between the prefrontal cortex (PFC) and hippocampus (HPC), in particular, are important for mnemonic processes. This is true during awake behavior, as well as during offline states like sleep. We have recently shown that the anatomically interposed thalamic nucleus reuniens (RE) has a role in coordinating slow-wave activity between the PFC and HPC. Here, we took advantage of spontaneous brain state changes occurring during urethane anesthesia in order to assess if PFC-HPC communication was modified during activated (theta) vs. deactivated (slow oscillation: SO) states. These forebrain states are highly similar to those expressed during rapid eye movement (REM) and non-REM stages of natural sleep, respectively. Evoked potentials and excitatory current sinks in the HPC were consistently larger during SO states, regardless of whether PFC or RE afferents were stimulated. Interestingly, PFC stimulation during theta appeared to preferentially use a cortico-cortical pathway, presumably involving the entorhinal cortex as opposed to the more direct RE to HPC conduit. Optogenetic and chemogenetic manipulations of the RE suggested that this state-dependent biasing was mediated by responding in the RE itself. Finally, the phase of both ongoing rhythms also appeared to be an important factor in modulating HPC responses, with maximal field excitatory postsynaptic potentials (EPSPs) occurring during the negative-going phase of both rhythms. Thus, forebrain state plays an important role in how communication takes place across the PFC and HPC, with the RE as a determining factor in how this is shaped. Furthermore, ongoing sleep-like rhythms influence the coordination and perhaps potentiate excitatory processing in this extended episodic memory circuit. Our results have direct implications for activity-dependent processes relevant to sleep-dependent memory consolidation.

Sleep spindles of non-REM sleep are transient, waxing-and-waning 10-16 Hz EEG oscillations, whose cortical synchronization depends on the engagement of thalamo-cortical loops. However, previous studies in animal models lacking the corpus callosum due to agenesis or total callosotomy and in humans with agenesis of the corpus callosum suggested that cortico-cortical connections may also have a relevant role in cortical (inter-hemispheric) spindle synchronization. Yet, most of these works did not provide direct quantitative analyses to support their observations. By studying a rare sample of callosotomized, split-brain patients, we recently demonstrated that the total resection of the corpus callosum is associated with a significant reduction in the inter-hemispheric propagation of non-REM slow waves. Interestingly, sleep spindles are often temporally and spatially grouped around slow waves (0.5-4 Hz), and this coordination is thought to have an important role in sleep-dependent learning and memory consolidation. Given these premises, here we set out to investigate whether total callosotomy may affect the generation and spreading of sleep spindles, as well as their coupling with sleep slow waves. To this aim, we analysed overnight high-density EEG recordings (256 electrodes) collected in five patients who underwent total callosotomy due to drug-resistant epilepsy (age 40-53, two females), three non-callosotomized neurological patients (age 44-66, two females), and in a sample of 24 healthy adult control subjects (age 20-47, 13 females). Individual sleep spindles were automatically detected using a validated algorithm and their properties and topographic distributions were computed. All analyses were performed with and without a regression-based adjustment accounting for inter-subject age differences. The comparison between callosotomized patients and healthy subjects did not reveal systematic variations in spindle density, amplitude or frequency. However, callosotomized patients were characterized by a reduced spindle duration, which could represent the result of a faster desynchronization of spindle activity across cortical areas of the two hemispheres. In contrast with our previous findings regarding sleep slow waves, we failed to detect in callosotomized patients any clear, systematic change in the inter-hemispheric synchronization of sleep spindles. In line with this, callosotomized patients were characterized by a reduced extension of the spatial association between temporally coupled spindles and slow waves. Our findings are consistent with a dependence of spindles on thalamo-cortical rather than cortico-cortical connections in humans, but also revealed that, despite their temporal association, slow waves and spindles are independently regulated in terms of topographic expression.

[This corrects the article DOI: 10.3389/fnins.2019.00316.].

Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the neocortex and large- and small- irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis to parse which populations are involved in different kinds of neocortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects neocortical deep layers, and is associated with larger amplitude slower neocortical oscillations. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the neocortex, suggesting hippocampal origin, and are associated with smaller amplitude faster neocortical oscillations. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.

Resting state network (RSN) functional connectivity (FC) has been investigated under a wealth of different healthy and compromised conditions. Such investigations are often dependent on the defined spatial boundaries and nodes of so-called canonical RSNs, themselves the product of extensive deliberations over distinctions between functional magnetic resonance imaging (fMRI) noise and neural signal, specifically in the context of the healthy waking state. However, a similar unbiased cataloging of noise and networks remains to be done in other states, particularly sleep, a healthy alternate mode of the brain that supports distinct operations from wakefulness, such as dreaming and memory consolidation. The purpose of this study was to explicitly test the hypothesis that there are RSNs unique to sleep. Simultaneous electroencephalography (EEG) and fMRI was used to record brain activity of non-sleep-deprived participants. Independent component analysis was performed on both rapid eye movement (REM; N = 7) and non-REM sleep stage fMRI data (non-REM2; N = 28, non-REM3; N = 11), with the resulting components spatially correlated with the canonical RSNs, for the purpose of identifying spatially distinct RSNs. Surprisingly, all low-correlation components were positively identified as noise, and all high-correlation components comprised the canonical set of RSNs typically observed in wake, indicating that sleep is supported by much the same RSN architecture as wakefulness, despite the unique operations performed during sleep. This further indicates that the implicit assumptions of prior studies, i.e. that the canonical RSNs apply to sleep FC analysis, are valid and have not overlooked sleep-specific RSNs.