The human K-complex represents an isolated cortical down-state

Sounding It Out: Auditory Stimulation and Overnight Memory Processing

Purpose of Review

Auditory stimulation is a technique that can enhance neural oscillations linked to overnight memory consolidation. In this review, we evaluate the impacts of auditory stimulation on the neural oscillations of sleep and associated memory processes in a variety of populations.

Recent Findings

Cortical EEG recordings of slow-wave sleep (SWS) are characterised by two cardinal oscillations: slow oscillations (SOs) and sleep spindles. Auditory stimulation delivered in SWS enhances SOs and phase-coupled spindle activity in healthy children and adults, children with ADHD, adults with mild cognitive impairment and patients with major depression. Under certain conditions, auditory stimulation bolsters the benefits of SWS for memory consolidation, although further work is required to fully understand the factors affecting stimulation-related memory gains. Recent work has turned to rapid eye movement (REM) sleep, demonstrating that auditory stimulation can be used to manipulate REM sleep theta oscillations.

Summary

Auditory stimulation enhances oscillations linked to overnight memory processing and shows promise as a technique for enhancing the memory benefits of sleep.

Microscale Physiological Events on the Human Cortical Surface

Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.

Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance-wakefulness (W), NREM-N2, NREM-N3, and REM sleep-with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5-4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Functional MRI of arousals in nonrapid eye movement sleep

Arousals commonly occur during human sleep and have been associated with several sleep disorders. Arousals are characterized as an abrupt electroencephalography (EEG) frequency change to higher frequencies during sleep. However, the human brain regions involved in arousal are not yet clear. Simultaneous EEG and functional magnetic resonance imaging (fMRI) data were recorded during the early portion of the sleep period in healthy young adults. Arousals were identified based on the EEG data, and fMRI signal changes associated with 83 arousals from 19 subjects were analyzed. Subcortical regions, including the midbrain, thalamus, basal ganglia, and cerebellum, were activated with arousal. Cortices, including the temporal gyrus, occipital gyrus, and frontal gyrus, were deactivated with arousal. The activations associated with arousal in the subcortical regions were consistent with previous findings of subcortical involvement in behavioral arousal and consciousness. Cortical deactivations may serve as a mechanism to direct incoming sensory stimuli to specific brain regions, thereby monitoring environmental perturbations during sleep.

An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex

BACKGROUND AND OBJECTIVE

K-complexes, as a significant indicator in sleep staging and sleep protection, are an important micro-event in sleep analysis. Clinically, K-complexes are recognized through the expert visual inspection of electroencephalogram (EEG) during sleep. Since this process is laborious and has high inter-observer variability, developing automated K-complex detection methods can alleviate the burden on clinicians while providing reliable recognition results. However, existing methods face the following issues. First, most work only identifies the K-complexes in stage 2, which requires distinguishing the sleep stages as the prerequisite for further events' identification. Second, most approaches can only detect the occurrence of events without the ability to predict their location and duration, which are also essential to sleep analysis.

METHODS

In this work, a novel hybrid expert scheme for K-complex detection is proposed by integrating signal morphology with expert knowledge into the decision-making process. To eliminate artifacts, and to minimize the individual variability in raw sleep EEG signals, the potential K-complex candidates are first screened by combining Teager energy operator (TEO) and personalized thresholds. Then, to distinguish signal shapes from background activity, a novel frame of filtering based on morphological filtering (MF) is devised to differentiate morphological components of K-complex waveforms from EEG series. Finally, K-complex waveforms are identified from the extracted morphological information by judgment rules, which are inspired by expert knowledge of micro-sleep events.

RESULTS

Detection performance is evaluated by its application on the public database MASS-C1 (Montreal archives of sleep studies cohort one) which includes the recordings of 19 healthy adults. The detection performance demonstrates an F-measure of 0.63 with a recall of 0.81 and a precision of 0.53 on average. The duration error between events and detections is 0.10 s.

CONCLUSIONS

The presented scheme has detected the occurrence of events. Meanwhile, it has recognized their locations and durations. The favorable results exhibit that the proposed scheme outperforms the state-of-the-art studies and has great potential to help release the burden of experts in sleep EEG analysis.

Focal lesions induce large-scale percolation of sleep-like intracerebral activity in awake humans

Focal cortical lesions are known to result in large-scale functional alterations involving distant areas; however, little is known about the electrophysiological mechanisms underlying these network effects. Here, we addressed this issue by analysing the short and long distance intracranial effects of controlled structural lesions in humans. The changes in Stereo-Electroencephalographic (SEEG) activity after Radiofrequency-Thermocoagulation (RFTC) recorded in 21 epileptic subjects were assessed with respect to baseline resting wakefulness and sleep activity. In addition, Cortico-Cortical Evoked Potentials (CCEPs) recorded before the lesion were employed to interpret these changes with respect to individual long-range connectivity patterns. We found that small structural ablations lead to the generation and large-scale propagation of sleep-like slow waves within the awake brain. These slow waves match those recorded in the same subjects during sleep, are prevalent in perilesional areas, but can percolate up to distances of 60 mm through specific long-range connections, as predicted by CCEPs. Given the known impact of slow waves on information processing and cortical plasticity, demonstrating their intrusion and percolation within the awake brain add key elements to our understanding of network dysfunction after cortical injuries.

K-complexes are a sensitive marker of noise-related sensory processing during sleep: A pilot study

STUDY OBJECTIVES

The primary aim of this study was to examine dose-response relationships between sound pressure levels (SPLs) and K-complex occurrence probability for wind farm and road traffic noise. A secondary aim was to compare K-complex dose-responses to manually scored EEG arousals and awakenings.

METHODS

Twenty-five participants underwent polysomnography recordings and noise exposure during sleep in a laboratory. Wind farm and road traffic noise recordings of 20-sec duration were played in random order at 6 SPLs between 33 - 48 dBA during established N2 or deeper sleep. Noise periods were separated with periods of 23 dBA background noise. K-complexes were scored using a validated algorithm. K-complex occurrence probability was compared between noise types controlling for noise SPL, subjective noise sensitivity and measured hearing acuity.

RESULTS

Noise-induced K-complexes were observed in N2 sleep at SPLs as low as 33 dBA (Odds ratio, 33dBA vs 23 dBA, mean (95% confidence interval); 1.75 (1.16, 2.66)) and increased with SPL. EEG arousals and awakenings were only associated with noise above 39 dBA in N2 sleep. K-complexes were 2 times more likely to occur in response to noise than EEG arousals or awakenings. Subjective noise sensitivity and hearing acuity were associated with K-complex occurrence, but not arousal or awakening. Noise type did not detectably influence K-complexes, EEG arousals or awakening responses.

CONCLUSION

These findings support that K-complexes are a sensitive marker of sensory processing of environmental noise during sleep and that increased hearing acuity and decreased self-reported noise sensitivity increase K-complex probability.

Local sleep-like cortical reactivity in the awake brain after focal injury

The functional consequences of focal brain injury are thought to be contingent on neuronal alterations extending beyond the area of structural damage. This phenomenon, also known as diaschisis, has clinical and metabolic correlates but lacks a clear electrophysiological counterpart, except for the long-standing evidence of a relative EEG slowing over the injured hemisphere. Here, we aim at testing whether this EEG slowing is linked to the pathological intrusion of sleep-like cortical dynamics within an awake brain. We used a combination of transcranial magnetic stimulation and electroencephalography (TMS/EEG) to study cortical reactivity in a cohort of 30 conscious awake patients with chronic focal and multifocal brain injuries of ischaemic, haemorrhagic and traumatic aetiology. We found that different patterns of cortical reactivity typically associated with different brain states (coma, sleep, wakefulness) can coexist within the same brain. Specifically, we detected the occurrence of prominent sleep-like TMS-evoked slow waves and off-periods—reflecting transient suppressions of neuronal activity—in the area surrounding focal cortical injuries. These perilesional sleep-like responses were associated with a local disruption of signal complexity whereas complex responses typical of the awake brain were present when stimulating the contralesional hemisphere. These results shed light on the electrophysiological properties of the tissue surrounding focal brain injuries in humans. Perilesional sleep-like off-periods can disrupt network activity but are potentially reversible, thus representing a principled read-out for the neurophysiological assessment of stroke patients, as well as an interesting target for rehabilitation.

Homeostatic sleep regulation in the absence of the circadian sleep-regulating component: effect of short light-dark cycles on sleep-wake stages and slow waves

BACKGROUND

Aside from the homeostatic and circadian components, light has itself an important, direct as well as indirect role in sleep regulation. Light exerts indirect sleep effect by modulating the circadian rhythms. Exposure to short light-dark cycle (LD 1:1, 1:1 h light - dark) eliminates the circadian sleep regulatory component but direct sleep effect of light could prevail. The aim of the present study was to examine the interaction between the light and the homeostatic influences regarding sleep regulation in a rat model.

METHODS

Spontaneous sleep-wake and homeostatic sleep regulation by sleep deprivation (SD) and analysis of slow waves (SW) were examined in Wistar rats exposed to LD1:1 condition using LD12:12 regime as control.

RESULTS

Slow wave sleep (SWS) and REM sleep were both enhanced, while wakefulness (W) was attenuated in LD1:1. SWS recovery after 6-h total SD was more intense in LD1:1 compared to LD12:12 and SWS compensation was augmented in the bright hours. Delta power increment during recovery was caused by the increase of SW number in both cases. More SW was seen during baseline in the second half of the day in LD1:1 and after SD compared to the LD12:12. Increase of SW number was greater in the bright hours compared to the dark ones after SD in LD1:1. Lights ON evoked immediate increase in W and decrease in both SWS and REM sleep during baseline LD1:1 condition, while these changes ceased after SD. Moreover, the initial decrease seen in SWS after lights ON, turned to an increase in the next 6-min bin and this increase was stronger after SD. These alterations were caused by the change of the epoch number in W, but not in case of SWS or REM sleep. Lights OFF did not alter sleep-wake times immediately, except W, which was increased by lights OFF after SD.

CONCLUSIONS

Present results show the complex interaction between light and homeostatic sleep regulation in the absence of the circadian component and indicate the decoupling of SW from the homeostatic sleep drive in LD1:1 lighting condition.

Current ideas about the roles of rapid eye movement and non-rapid eye movement sleep in brain development

Understanding the links between sleep and brain development is important, as rapid eye movement (REM) sleep and non-REM (NREM) sleep seem to contribute to different aspects of brain maturation. If children have sleep problems, REM sleep and NREM sleep are likely to have different consequences for their developing brain, depending on their age. We highlight important discoveries from human and animal research on the role sleep plays in brain development. A hypothetical model is presented to explain the dynamic relationship of REM sleep and NREM sleep with different processes of brain maturation, with implications for current neonatal care and future research.

Sex differences in blood pressure responsiveness to spontaneous K-complexes during stage II sleep

K-complexes are a key marker of nonrapid eye movement sleep, specifically during stages II sleep. Recent evidence suggests the heart rate responses to a K-complexes may differ between men and women. The purpose of this study was to compare beat-to-beat blood pressure responses to K-complexes in men and women. We hypothesized that the pressor response following a spontaneous K-complex would be augmented in men compared with women. Ten men [age: 23 ± 2 yr, body mass index (BMI): 28 ± 4 kg/m²] and ten women (age: 23 ± 5 yr, BMI: 25 ± 4 kg/m²) were equipped with overnight finger plethysmography and standard 10-lead polysomnography. Hemodynamic responses to a spontaneous K-complex during stable stage II sleep were quantified for 10 consecutive cardiac cycles, and measurements included systolic arterial pressure (SAP), diastolic arterial pressure (DAP), and heart rate. K-complex elicited greater pressor responses in men when blood pressures were expressed as SAP (cardiac cycle × sex: P = 0.007) and DAP (cardiac cycle × sex: P = 0.004). Heart rate trended to be different between men and women (cardiac cycle × sex: P = 0.078). These findings suggest a divergent pressor response between men and women following a spontaneous K-complex during normal stage II sleep. These findings could contribute to sex-specific differences in cardiovascular risk that exist between men and women.NEW & NOTEWORTHY K-complexes during stage II sleep have been shown to elicit acute increases in blood pressure and heart rate, but the role of sex (i.e., male vs. female) in this response is unclear. In the present study, we demonstrate that the pressor response following spontaneous K-complexes were augmented in men compared to age-matched women. The augmented blood pressure reactivity to spontaneous K-complexes during stage II sleep in men advance the field of cardiovascular sex differences, with implications for nocturnal blood pressure control.

Sleep and Memory in Children

Purpose of Review

This short review article aims at emphasizing interesting and important new insights about investigating sleep and memory in children aged between 6 and 13 years (middle childhood).

Recent Findings

That sleep in comparison to wakefulness benefits the consolidation of memories is well established—especially for the adult population. However, the underlying theoretical frameworks trying to explain the benefits of sleep for memory still strive for more substantiate findings including biological and physiological correlates.

Summary

Based on the most recent literature about sleep-related memory consolidation and its physiological markers during middle childhood, this article provides a review and highlights recent updates in this field.

The laminar profile of sleep spindles in humans

Sleep spindles are functionally important NREM sleep EEG oscillations which are generated in thalamocortical, corticothalamic and possibly cortico-cortical circuits. Previous hypotheses suggested that slow and fast spindles or spindles with various spatial extent may be generated in different circuits with various cortical laminar innervation patterns. We used NREM sleep EEG data recorded from four human epileptic patients undergoing presurgical electrophysiological monitoring with subdural electrocorticographic grids (ECoG) and implanted laminar microelectrodes penetrating the cortex (IME). The position of IMEs within cortical layers was confirmed using postsurgical histological reconstructions. Many spindles detected on the IME occurred only in one layer and were absent from the ECoG, but with increasing amplitude simultaneous detection in other layers and on the ECoG became more likely. ECoG spindles were in contrast usually accompanied by IME spindles. Neither IME nor ECoG spindle cortical profiles were strongly associated with sleep spindle frequency or globality. Multiple-unit and single-unit activity during spindles, however, was heterogeneous across spindle types, but also across layers and patients. Our results indicate that extremely local spindles may occur in any cortical layer, but co-occurrence at other locations becomes likelier with increasing amplitude and the relatively large spindles detected on ECoG channels have a stereotypical laminar profile. We found no compelling evidence that different spindle types are associated with different laminar profiles, suggesting that they are generated in cortical and thalamic circuits with similar cortical innervation patterns. Local neuronal activity is a stronger candidate mechanism for driving functional differences between spindles subtypes.

Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning

STUDY OBJECTIVES

K-complexes (KCs) are a recognized electroencephalography marker of sensory processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging, and a range of sleep and sensory processing deficits. However, manual scoring of K-complexes is impractical, time-consuming, and thus costly and currently not well-standardized. Although automated KC detection methods have been developed, performance and uptake remain limited.

METHODS

The proposed algorithm is based on a deep neural network and Gaussian process, which gives the input waveform a probability of being a KC ranging from 0% to 100%. The algorithm was trained on half a million synthetic KCs derived from manually scored sleep stage 2 KCs from the Montreal Archive of Sleep Study containing 19 healthy young participants. Algorithm performance was subsequently assessed on 700 independent recordings from the Cleveland Family Study using sleep stages 2 and 3 data.

RESULTS

The developed algorithm showed an F1 score (a measure of binary classification accuracy) of 0.78 and thus outperforms currently available KC scoring algorithms with F1 = 0.2-0.6. The probabilistic approach also captured expected variability in KC shape and amplitude within individuals and across age groups.

CONCLUSIONS

An automated probabilistic KC classification is well suited and effective for systematic KC detection for a more in-depth exploration of potential relationships between KCs during sleep and clinical outcomes such as health impacts and daytime symptomatology.

Brain networks involved in generalized periodic discharges (GPD) in post-anoxic-ischemic encephalopathy

AIM

Generalized periodic discharge (GPD) is an EEG pattern of poor neurological outcome, frequently observed in comatose patients after cardiac arrest. The aim of our study was to identify the neuronal network generating ≤2.5 Hz GPD using EEG source localization and connectivity analysis.

METHODS

We analyzed 40 comatose adult patients with anoxic-ischemic encephalopathy, who had 19 channel-EEG recording. We computed electric source analysis based on distributed inverse solution (LAURA) and we estimated cortical activity in 82 atlas-based cortical brain regions. We applied directed connectivity analysis (Partial Directed Coherence) on these sources to estimate the main drivers.

RESULTS

Source analysis suggested that the GPD are generated in the cortex of the limbic system in the majority of patients (87.5%). Connectivity analysis revealed main drivers located in thalamus and hippocampus for the large majority of patients (80%), together with important activation also in amygdala (70%).

CONCLUSIONS

We hypothesize that the anoxic-ischemic dysfunction, leading to hyperactivity of the thalamo-cortical (limbic presumably) circuit, can result in an oscillatory thalamic activity capable of inducing periodic cortical (limbic, mostly medial-temporal and orbitofrontal) discharges, similarly to the case of generalized rhythmic spike-wave discharge in convulsive or non-convulsive status epilepticus.

The subcortical belly of sleep: New possibilities in neuromodulation of basal ganglia?

Early studies posited a relationship between sleep and the basal ganglia, but this relationship has received little attention recently. It is timely to revisit this relationship, given new insights into the functional anatomy of the basal ganglia and the physiology of sleep, which has been made possible by modern techniques such as chemogenetic and optogenetic mapping of neural circuits in rodents and intracranial recording, functional imaging, and a better understanding of human sleep disorders. We discuss the functional anatomy of the basal ganglia, and review evidence implicating their role in sleep. Whilst these studies are in their infancy, we suggest that the basal ganglia may play an integral role in the sleep-wake cycle, specifically by contributing to a thalamo-cortical-basal ganglia oscillatory network in slow-wave sleep which facilitates neural plasticity, and an active state during REM sleep which enables the enactment of cognitive and emotional networks. A better understanding of sleep mechanisms may pave the way for more effective neuromodulation strategies for sleep and basal ganglia disorders.

Slow Wave Sleep Is a Promising Intervention Target for Alzheimer's Disease

Alzheimer's disease (AD) is the major cause of dementia, characterized by the presence of amyloid-beta plaques and neurofibrillary tau tangles. Plaques and tangles are associated with sleep-wake cycle disruptions, including the disruptions in non-rapid eye movement (NREM) slow wave sleep (SWS). Alzheimer's patients spend less time in NREM sleep and exhibit decreased slow wave activity (SWA). Consistent with the critical role of SWS in memory consolidation, reduced SWA is associated with impaired memory consolidation in AD patients. The aberrant SWA can be modeled in transgenic mouse models of amyloidosis and tauopathy. Animal models exhibited slow wave impairments early in the disease progression, prior to the deposition of amyloid-beta plaques, however, in the presence of abundant oligomeric amyloid-beta. Optogenetic rescue of SWA successfully halted the amyloid accumulation and restored intraneuronal calcium levels in mice. On the other hand, optogenetic acceleration of slow wave frequency exacerbated amyloid deposition and disrupted neuronal calcium homeostasis. In this review, we summarize the evidence and the mechanisms underlying the existence of a positive feedback loop between amyloid/tau pathology and SWA disruptions that lead to further accumulations of amyloid and tau in AD. Moreover, since SWA disruptions occur prior to the plaque deposition, SWA disruptions may provide an early biomarker for AD. Finally, we propose that therapeutic targeting of SWA in AD might lead to an effective treatment for Alzheimer's patients.

A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions

Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep's effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with 'sleep and stimulation' and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.

EEG microstates are correlated with brain functional networks during slow-wave sleep

Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the "atoms of thought". Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.

The human K-complex: Insights from combined scalp-intracranial EEG recordings

Sleep spindles and K-complexes (KCs) are a hallmark of N2 sleep. While the functional significance of spindles is comparatively well investigated, there is still ongoing debate about the role of the KC: it is unclear whether it is a cortical response to an arousing stimulus (either external or internal) or whether it has sleep-promoting properties. Invasive intracranial EEG recordings from individuals with drug-resistant epilepsy offer a unique opportunity to study in-situ human brain physiology. To better understand the function of the KC, we aimed to (i) investigate the intracranial correlates of spontaneous scalp KCs, and (ii) compare the intracranial activity of scalp KCs associated or not with arousals. Whole-night recordings from adults with drug-resistant focal epilepsy who underwent combined intracranial-scalp EEG for pre-surgical evaluation at the Montreal Neurological Institute between 2010 and 2018 were selected. KCs were visually marked in the scalp and categorized according to the presence of microarousals: (i) Pre-microarousal KCs; (ii) KCs during an ongoing microarousal; and (iii) KCs without microarousal. Power in different spectral bands was computed to compare physiological intracranial EEG activity at the time of scalp KCs relative to the background, as well as to compare microarousal subcategories. A total of 1198 scalp KCs selected from 40 subjects were analyzed, resulting in 32,504 intracranial KC segments across 992 channels. Forty-seven percent of KCs were without microarousal, 30% were pre-microarousal, and 23% occurred during microarousals. All scalp KCs were accompanied by widespread cortical increases in delta band power (0.3-4 ​Hz) relative to the background: the highest percentages were observed in the parietal (60-65%) and frontal cortices (52-58%). Compared to KCs without microarousal, pre-microarousal KCs were accompanied by increases (66%) in beta band power (16-30 ​Hz) in the motor cortex, which was present before the peak of the KC. In addition, spatial distribution of spectral power changes following each KC without microarousal revealed that certain brain regions were associated with increases in delta power (25-62%) or decreases in alpha/beta power (11-24%), suggesting a sleep-promoting pattern, whereas others were accompanied by increases of higher frequencies (12-27%), suggesting an arousal-related pattern. This study shows that KCs can be generated across widespread cortical areas. Interestingly, the motor cortex shows awake-like EEG activity before the onset of KCs followed by microarousals. Our findings also highlight region-specific sleep- or arousal-promoting responses following KCs, suggesting a dual role for the human KC.

A wake-active locomotion circuit depolarizes a sleep-active neuron to switch on sleep

Sleep-active neurons depolarize during sleep to suppress wakefulness circuits. Wake-active wake-promoting neurons in turn shut down sleep-active neurons, thus forming a bipartite flip-flop switch. However, how sleep is switched on is unclear because it is not known how wakefulness is translated into sleep-active neuron depolarization when the system is set to sleep. Using optogenetics in Caenorhabditis elegans, we solved the presynaptic circuit for depolarization of the sleep-active RIS neuron during developmentally regulated sleep, also known as lethargus. Surprisingly, we found that RIS activation requires neurons that have known roles in wakefulness and locomotion behavior. The RIM interneurons—which are active during and can induce reverse locomotion—play a complex role and can act as inhibitors of RIS when they are strongly depolarized and as activators of RIS when they are modestly depolarized. The PVC command interneurons, which are known to promote forward locomotion during wakefulness, act as major activators of RIS. The properties of these locomotion neurons are modulated during lethargus. The RIMs become less excitable. The PVCs become resistant to inhibition and have an increased capacity to activate RIS. Separate activation of neither the PVCs nor the RIMs appears to be sufficient for sleep induction; instead, our data suggest that they act in concert to activate RIS. Forward and reverse circuit activity is normally mutually exclusive. Our data suggest that RIS may be activated at the transition between forward and reverse locomotion states, perhaps when both forward (PVC) and reverse (including RIM) circuit activity overlap. While RIS is not strongly activated outside of lethargus, altered activity of the locomotion interneurons during lethargus favors strong RIS activation and thus sleep. The control of sleep-active neurons by locomotion circuits suggests that sleep control may have evolved from locomotion control. The flip-flop sleep switch in C. elegans thus requires an additional component, wake-active sleep-promoting neurons that translate wakefulness into the depolarization of a sleep-active neuron when the worm is sleepy. Wake-active sleep-promoting circuits may also be required for sleep state switching in other animals, including in mammals.

This study in nematodes shows that to understand sleep state switching, the flip-flop model for sleep regulation needs to be complemented by additional wake-active sleep-promoting neurons that activate sleep-active sleep-promoting neurons to induce sleep.

Association between Loss of Sleep-specific Waves and Age, Sleep Efficiency, Body Mass Index, and Apnea-Hypopnea Index in Human N3 Sleep

Sleep spindles (SS) and K-complexes (KC) play important roles in human sleep. It has been reported that age, body mass index (BMI), and apnea-hypopnea index (AHI) may influence the number of SS or KC in non-rapid-eye-movement (NREM) 2 (N2) sleep. In this study, we investigated whether the loss of SS or KC is associated with the above factors in NREM 3 (N3) sleep. A total of 152 cases were enrolled from 2013 to 2017. The correlations between the number of SS or KC in N3 sleep and participants’ characteristics were analyzed using Spearman rank correlation. Chi-squared test was used to assess the effects of age, sleep efficiency, and BMI on the loss of N3 sleep, N3 spindle and N3 KC. Our results showed that there were negative correlations between the number of SS in N3 sleep with age, BMI, and AHI (P < 0.001), and similar trends were found for KC as well. The loss of SS and KC in N3 sleep was related with age, BMI, and AHI (P < 0.01), as was the loss of N3 sleep (P < 0.01). However, sleep efficiency was not related with the loss of N3 sleep, SS and KC in N3 sleep (P > 0.05). The present study supports that age, BMI, and AHI are all influencing factors of SS and KC loss in human N3 sleep, but sleep efficiency was not an influencing factor in the loss of N3 sleep and the loss of SS and KC in N3 sleep.

Spatio-temporal properties of sleep slow waves and implications for development

Objective sleep quality can be measured by electroencephalography (EEG), a non-invasive technique to quantify electrical activity generated by the brain. With EEG, sleep depth is measured by appearance and an increase in slow wave activity (scalp-SWA). EEG slow waves (scalp-SW) are the manifestation of underlying synchronous membrane potential transitions between silent (DOWN) and active (UP) states. This bistable periodic rhythm is defined as slow oscillation (SO). During its "silent state" cortical neurons are hyperpolarized and appear inactive, while during its "active state" cortical neurons are depolarized, fire spikes and exhibit continuous synaptic activity, excitatory and inhibitory. In adults, data from high-density EEG revealed that scalp-SW propagate across the cortical mantle in complex patterns. However, scalp-SW propagation undergoes modifications across development. We present novel data from children, indicating that scalp-SW originate centro-parietally, and emerge more frontally by adolescence. Based on the concept that SO and SW could actively modify neuronal connectivity, we discuss whether they fulfill a key purpose in brain development by actively conveying modifications of the maturing brain.

Epidermal Growth Factor Signaling Promotes Sleep through a Combined Series and Parallel Neural Circuit

Sleep requires sleep-active neurons that depolarize to inhibit wake circuits. Sleep-active neurons are under the control of homeostatic mechanisms that determine sleep need. However, little is known about the molecular and circuit mechanisms that translate sleep need into the depolarization of sleep-active neurons. During many stages and conditions in C. elegans, sleep requires a sleep-active neuron called RIS. Here, we defined the transcriptome of RIS and discovered that genes of the epidermal growth factor receptor (EGFR) signaling pathway are expressed in RIS. Because of cellular stress, EGFR directly activates RIS. Activation of EGFR signaling in the ALA neuron has previously been suggested to promote sleep independently of RIS. Unexpectedly, we found that ALA activation promotes RIS depolarization. Our results suggest that ALA is a drowsiness neuron with two separable functions: (1) it inhibits specific behaviors, such as feeding, independently of RIS, (2) and it activates RIS. Whereas ALA plays a strong role in surviving cellular stress, surprisingly, RIS does not. In summary, EGFR signaling can depolarize RIS by an indirect mechanism through activation of the ALA neuron that acts upstream of the sleep-active RIS neuron and through a direct mechanism using EGFR signaling in RIS. ALA-dependent drowsiness, rather than RIS-dependent sleep bouts, appears to be important for increasing survival after cellular stress, suggesting that different types of behavioral inhibition play different roles in restoring health. VIDEO ABSTRACT.

Bridging Single Neuron Dynamics to Global Brain States

Biological neural networks produce information backgrounds of multi-scale spontaneous activity that become more complex in brain states displaying higher capacities for cognition, for instance, attentive awake versus asleep or anesthetized states. Here, we review brain state-dependent mechanisms spanning ion channel currents (microscale) to the dynamics of brain-wide, distributed, transient functional assemblies (macroscale). Not unlike how microscopic interactions between molecules underlie structures formed in macroscopic states of matter, using statistical physics, the dynamics of microscopic neural phenomena can be linked to macroscopic brain dynamics through mesoscopic scales. Beyond spontaneous dynamics, it is observed that stimuli evoke collapses of complexity, most remarkable over high dimensional, asynchronous, irregular background dynamics during consciousness. In contrast, complexity may not be further collapsed beyond synchrony and regularity characteristic of unconscious spontaneous activity. We propose that increased dimensionality of spontaneous dynamics during conscious states supports responsiveness, enhancing neural networks' emergent capacity to robustly encode information over multiple scales.

Functional connectivity dynamics slow with descent from wakefulness to sleep

The transition from wakefulness to sleep is accompanied by widespread changes in brain functioning. Here we investigate the implications of this transition for interregional functional connectivity and their dynamic changes over time. Simultaneous EEG-fMRI was used to measure brain functional activity of 21 healthy participants as they transitioned from wakefulness into sleep. fMRI volumes were independent component analysis (ICA)-decomposed, yielding 42 neurophysiological sources. Static functional connectivity (FC) was estimated from independent component time courses. A sliding window method and k-means clustering (k = 7, L2-norm) were used to estimate dynamic FC. Static FC in Wake and Stage-2 Sleep (NREM2) were largely similar. By contrast, FC dynamics across wake and sleep differed, with transitions between FC states occurring more frequently during wakefulness than during NREM2. Evidence of slower FC dynamics during sleep is discussed in relation to sleep-related reductions in effective connectivity and synaptic strength.

The asynchronous state's relation to large-scale potentials in cortex

Understanding the relation between large-scale potentials (M/EEG) and their underlying neural activity can improve the precision of research and clinical diagnosis. Recent insights into cortical dynamics highlighted a state of strongly reduced spike count correlations, termed the asynchronous state (AS). The AS has received considerable attention from experimenters and theorists alike, regarding its implications for cortical dynamics and coding of information. However, how reconcilable are these vanishing correlations in the AS with large-scale potentials such as M/EEG observed in most experiments? Typically the latter are assumed to be based on underlying correlations in activity, in particular between subthreshold potentials. We survey the occurrence of the AS across brain states, regions, and layers and argue for a reconciliation of this seeming disparity: large-scale potentials are either observed, first, at transitions between cortical activity states, which entail transient changes in population firing rate, as well as during the AS, and, second, on the basis of sufficiently large, asynchronous populations that only need to exhibit weak correlations in activity. Cells with no or little spiking activity can contribute to large-scale potentials via their subthreshold currents, while they do not contribute to the estimation of spiking correlations, defining the AS. Furthermore, third, the AS occurs only within particular cortical regions and layers associated with the currently selected modality, allowing for correlations at other times and between other areas and layers.

Improved identification and differentiation from epileptiform activity of human hippocampal sharp wave ripples during NREM sleep

In rodents, pyramidal cell firing patterns from waking may be replayed in nonrapid eye movement sleep (NREM) sleep during hippocampal sharp wave ripples (HC-SWR). In humans, HC-SWR have only been recorded with electrodes implanted to localize epileptogenicity. Here, we characterize human HC-SWR with rigorous rejection of epileptiform activity, requiring multiple oscillations and coordinated sharp waves. We demonstrated typical SWR in those rare HC recordings which lack interictal epileptiform spikes (IIS) and with no or minimal seizure involvement. These HC-SWR have a similar rate (~12 min^-1 on average, variable across NREM stages and anterior/posterior HC) and apparent intra-HC topography (ripple maximum in putative stratum pyramidale, slow wave in radiatum) as rodents, though with lower frequency (~85 Hz compared to ~140 Hz in rodents). Similar SWR are found in HC with IIS, but no significant seizure involvement. These SWR were modulated by behavior, being largely absent (<2 min^-1 ) except during NREM sleep in both Stage 2 (~9 min^-1 ) and Stage 3 (~15 min^-1 ), distinguishing them from IIS. This study quantifies the basic characteristics of a strictly selected sample of SWR recorded in relatively healthy human hippocampi.

Spontaneous K-Complexes may be biomarkers of the progression of amnestic mild cognitive impairment

OBJECTIVE

Spontaneous K-complexes (SKCs), a hallmark of stage 2 sleep, have been reported to decrease in density in Alzheimer's disease (AD) patients. However, few former studies have explored the alterations in SKC characteristics in the pre-clinical phase of AD-amnestic mild cognitive impairment (aMCI). The aim of our prospective cohort study was to investigate the changing trend in SKC characteristics during the progression of aMCI.

METHODS

SKC density, amplitude and duration were measured in aMCI subjects and normal controls (NC) at two-year follow-up assessments by polysomnography (PSG). In sum, 22 NCs, 25 stable aMCI (sMCI) subjects and 20 progressive aMCI (pMCI) subjects finished the four follow-up PSG assessments, and their data were used for analysis.

RESULTS

SKC density and amplitude, but not duration, decreased during the follow-up assessments in both NCs and aMCI subjects, but the rate of decrease of these parameters was greater in aMCI subjects. With the progression of aMCI, significant differences in SKC density and amplitude among the three groups were observed, whereas SKC density showed no difference at the early stage of aMCI. The receiver operating characteristic (ROC) curve results demonstrated that SKC density and amplitude could distinguish aMCI subjects from NCs with high specificity and sensitivity.

CONCLUSION

Our results suggest that SKCs decrease with ageing and the progression of aMCI, and SKC characteristics may be potential biomarkers for diagnosing aMCI.

Distributed slow-wave dynamics during sleep predict memory consolidation and its impairment in schizophrenia

The slow waves (SW) of non-rapid eye movement (NREM) sleep reflect neocortical components of network activity during sleep-dependent information processing; their disruption may therefore impair memory consolidation. Here, we quantify sleep-dependent consolidation of motor sequence memory, alongside sleep EEG-derived SW properties and synchronisation, and SW–spindle coupling in 21 patients suffering from schizophrenia and 19 healthy volunteers. Impaired memory consolidation in patients culminated in an overnight improvement in motor sequence task performance of only 1.6%, compared with 15% in controls. During sleep after learning, SW amplitudes and densities were comparable in healthy controls and patients. However, healthy controls showed a significant 45% increase in frontal-to-occipital SW coherence during sleep after motor learning in comparison with a baseline night (baseline: 0.22 ± 0.05, learning: 0.32 ± 0.05); patient EEG failed to show this increase (baseline: 0.22 ± 0.04, learning: 0.19 ± 0.04). The experience-dependent nesting of spindles in SW was similarly disrupted in patients: frontal-to-occipital SW–spindle phase-amplitude coupling (PAC) significantly increased after learning in healthy controls (modulation index baseline: 0.17 ± 0.02, learning: 0.22 ± 0.02) but not in patients (baseline: 0.13 ± 0.02, learning: 0.14 ± 0.02). Partial least-squares regression modelling of coherence and PAC data from all electrode pairs confirmed distributed SW coherence and SW–spindle coordination as superior predictors of overnight memory consolidation in healthy controls but not in patients. Quantifying the full repertoire of NREM EEG oscillations and their long-range covariance therefore presents learning-dependent changes in distributed SW and spindle coordination as fingerprints of impaired cognition in schizophrenia.

Mental Sleep Activity and Disturbing Dreams in the Lifespan

Sleep significantly changes across the lifespan, and several studies underline its crucial role in cognitive functioning. Similarly, mental activity during sleep tends to covary with age. This review aims to analyze the characteristics of dreaming and disturbing dreams at different age brackets. On the one hand, dreams may be considered an expression of brain maturation and cognitive development, showing relations with memory and visuo-spatial abilities. Some investigations reveal that specific electrophysiological patterns, such as frontal theta oscillations, underlie dreams during sleep, as well as episodic memories in the waking state, both in young and older adults. On the other hand, considering the role of dreaming in emotional processing and regulation, the available literature suggests that mental sleep activity could have a beneficial role when stressful events occur at different age ranges. We highlight that nightmares and bad dreams might represent an attempt to cope the adverse events, and the degrees of cognitive-brain maturation could impact on these mechanisms across the lifespan. Future investigations are necessary to clarify these relations. Clinical protocols could be designed to improve cognitive functioning and emotional regulation by modifying the dream contents or the ability to recall/non-recall them.

Large-scale structure and individual fingerprints of locally coupled sleep oscillations

Slow oscillations and sleep spindles, the canonical electrophysiological oscillations of nonrapid eye movement sleep, are thought to gate incoming sensory information, underlie processes of sleep-dependent memory consolidation, and are altered in various neuropsychiatric disorders. Accumulating evidence of the predominantly local expression of these individual oscillatory rhythms suggests that their cross-frequency interactions may have a similar local component. However, it is unclear whether locally coordinated sleep oscillations exist across the cortex, and whether and how these dynamics differ between fast and slow spindles, and sleep stages. Moreover, substantial individual variability in the expression of both spindles and slow oscillations raises the possibility that their temporal organization shows similar individual differences. Using two nights of multichannel electroencephalography recordings from 24 healthy individuals, we characterized the topography of slow oscillation-spindle coupling. We found that while slow oscillations are highly restricted in spatial extent, the phase of the local slow oscillation modulates local spindle activity at virtually every cortical site. However, coupling dynamics varied with spindle class, sleep stage, and cortical region. Moreover, the slow oscillation phase at which spindles were maximally expressed differed markedly across individuals while remaining stable across nights. These findings both add an important spatial aspect to our understanding of the temporal coupling of sleep oscillations and demonstrate the heterogeneity of coupling dynamics, which must be taken into account when formulating mechanistic accounts of sleep-related memory processing.

The Neurophysiological Effect of Acoustic Stimulation with Real-time Sleep Spindle Detection

Sleep spindle is a salient brain activity found in the sigma frequency range (11-16 Hz) during sleep stage 2. It has been demonstrated that sleep spindle is related to memory consolidation, neurodegenerative disease, and mental disorders. Slow wave activity (0.5-4 Hz) is the most prominent EEG activity during sleep and appears as a large, spontaneous synchronization of cortical neurons. The role of slow wave activity has been proposed to regulate synaptic strength and memory consolidation. Many studies have investigated the effect of acoustic stimuli during the sleep slow wave. However, there have been few studies which investigated an effect of acoustic stimulation during sleep spindle activity. In this study, we examined the neurophysiological effect of acoustic stimulation during sleep spindle activity. We delivered pink noise after the detection of sleep spindle, and surmised that acoustic stimulation after sleep spindle detection may preserve delta activity during ongoing sleep. Further, we observed suppression of the sleep spindle activity around the times of acoustic stimulation and evoked slow wave activity and theta band activity immediately after tone onset.

Sleep-Wake Cycle in Young and Older Mice

Sleep plays a key role in multiple cognitive functions and sleep pattern changes with aging. Human studies revealed that aging decreases sleep efficiency and reduces the total sleep time, the time spent in slow-wave sleep (SWS), and the delta power (1–4 Hz) during sleep; however, some studies of sleep and aging in mice reported opposing results. The aim of our work is to estimate how features of sleep–wake state in mice during aging could correspond to age-dependent changes observed in human. In this study, we investigated the sleep/wake cycle in young (3 months old) and older (12 months old) C57BL/6 mice using local-field potentials (LFPs). We found that older adult mice sleep more than young ones but only during the dark phase of sleep-wake cycle. Sleep fragmentation and sleep during the active phase (dark phase of cycle), homologous to naps, were higher in older mice. Older mice show a higher delta power in frontal cortex, which was accompanied with similar trend for age differences in slow wave density. We also investigated regional specificity of sleep–wake electrographic activities and found that globally posterior regions of the cortex show more rapid eye movement (REM) sleep whereas somatosensory cortex displays more often SWS patterns. Our results indicate that the effects of aging on the sleep–wake activities in mice occur mainly during the dark phase and the electrode location strongly influence the state detection. Despite some differences in sleep–wake cycle during aging between human and mice, some features of mice sleep share similarity with human sleep during aging.

Coordination of Human Hippocampal Sharpwave Ripples during NREM Sleep with Cortical Theta Bursts, Spindles, Downstates, and Upstates

In rodents, waking firing patterns replay in NREM sleep during hippocampal sharpwave ripples (HC-SWRs), correlated with neocortical graphoelements (NC-GEs). NC-GEs include theta bursts, spindles, downstates, and upstates. In humans, consolidation during sleep is correlated with scalp-recorded spindles and downstates/upstates, but HC-SWRs cannot be recorded noninvasively. Here we show in humans of both sexes that HC-SWRs are highly correlated with NC-GEs during NREM, with significantly more related HC-SWRs/NC-GEs for downstates or upstates than theta bursts or spindles, in N2 than N3, in posterior than anterior HC, in frontal than occipital cortex, and ipsilaterally than contralaterally. The preferences interacted (e.g., frontal spindles co-occurred frequently with posterior HC-SWRs in N2). These preferred GEs, stages, and locations for HC-SWR/NC-GE interactions may index selective consolidation activity, although that was not tested in this study. SWR recorded in different HC regions seldom co-occurred, and were related to GE in different cortical areas, showing that HC-NC interact in multiple transient, widespread but discrete, networks. NC-GEs tend to occur with consistent temporal relationships to HC-SWRs, and to each other. Cortical theta bursts usually precede HC-SWRs, where they may help define cortical input triggering HC-SWR firing. HC-SWRs often follow cortical downstate onsets, surrounded by locally decreased broadband power, suggesting a mechanism synchronizing cortical, thalamic, and hippocampal activities. Widespread cortical upstates and spindles follow HC-SWRs, consistent with the hypothesized contribution by hippocampal firing during HC-SWRs to cortical firing-patterns during upstates and spindles. Overall, our results describe how hippocampal and cortical oscillations are coordinated in humans during events that are critical for memory consolidation in rodents.SIGNIFICANCE STATEMENT Hippocampal sharpwave ripples, essential for memory consolidation, mark when hippocampal neurons replay waking firing patterns. In rodents, cortical sleep waves coordinate the transfer of temporary hippocampal to permanent cortical memories, but their relationship with human hippocampal sharpwave ripples remains unclear. We show that human hippocampal sharpwave ripples co-occur with all varieties of cortical sleep waves, in all cortical regions, and in all stages of NREM sleep, but with overall preferences for each of these. We found that sharpwave ripples in different parts of the hippocampus usually occurred independently of each other, and preferentially interacted with different cortical areas. We found that sharpwave ripples typically occur after certain types of cortical waves, and before others, suggesting how the cortico-hippocampo-cortical interaction may be organized in time and space.

Posterior Hippocampal Spindle Ripples Co-occur with Neocortical Theta Bursts and Downstates-Upstates, and Phase-Lock with Parietal Spindles during NREM Sleep in Humans

Human anterior and posterior hippocampus (aHC, pHC) differ in connectivity and behavioral correlates. Here we report physiological differences in humans of both sexes. During NREM sleep, the human hippocampus generates sharpwave ripples (SWRs) similar to those which in rodents mark memory replay. We show that while pHC generates SWRs, it also generates approximately as many spindle ripples (SSR: ripples phase-locked to local spindles). In contrast, SSRs are rare in aHC. Like SWRs, SSRs often co-occur with neocortical theta bursts (TBs), downstates (DSs), sleep spindles (SSs), and upstates (USs), which coordinate cortico-hippocampal interactions and facilitate consolidation in rodents. SWRs co-occur with these waves in widespread cortical areas, especially frontocentral. These waves typically occur in the sequence TB-DS-SS-US, with SWRs usually occurring before SS-US. In contrast, SSRs occur ∼350 ms later, with a strong preference for co-occurrence with posterior-parietal SSs. pHC-SSs were strongly phase-locked with parietal-SSs, and pHC-SSRs were phase-coupled with pHC-SSs and parietal-SSs. Human SWRs (and associated replay events, if any) are separated by ∼5 s on average, whereas ripples on successive SSR peaks are separated by only ∼80 ms. These distinctive physiological properties of pHC-SSR enable an alternative mechanism for hippocampal engagement with neocortex.SIGNIFICANCE STATEMENT Rodent hippocampal neurons replay waking events during sharpwave ripples (SWRs) in NREM sleep, facilitating memory transfer to a permanent cortical store. We show that human anterior hippocampus also produces SWRs, but spindle ripples predominate in posterior. Whereas SWRs typically occur as cortex emerges from inactivity, spindle ripples typically occur at peak cortical activity. Furthermore, posterior hippocampal spindle ripples are tightly coupled to posterior parietal locations activated by conscious recollection. Finally, multiple spindle ripples can recur within a second, whereas SWRs are separated by ∼5 s. The human posterior hippocampus is considered homologous to rodent dorsal hippocampus, which is thought to be specialized for consolidation of specific memory details. We speculate that these distinct physiological characteristics of posterior hippocampal spindle ripples may support a related function in humans.

Prenatal Nicotine Exposure Disrupts Infant Neural Markers of Orienting

Introduction

Prenatal nicotine exposure (PNE) from maternal cigarette smoking is linked to developmental deficits, including impaired auditory processing, language, generalized intelligence, attention, and sleep. Fetal brain undergoes massive growth, organization, and connectivity during gestation, making it particularly vulnerable to neurotoxic insult. Nicotine binds to nicotinic acetylcholine receptors, which are extensively involved in growth, connectivity, and function of developing neural circuitry and neurotransmitter systems. Thus, PNE may have long-term impact on neurobehavioral development. The purpose of this study was to compare the auditory K-complex, an event-related potential reflective of auditory gating, sleep preservation and memory consolidation during sleep, in infants with and without PNE and to relate these neural correlates to neurobehavioral development.

Methods

We compared brain responses to an auditory paired-click paradigm in 3- to 5-month-old infants during Stage 2 sleep, when the K-complex is best observed. We measured component amplitude and delta activity during the K-complex.

Results

Infants with PNE demonstrated significantly smaller amplitude of the N550 component and reduced delta-band power within elicited K-complexes compared to nonexposed infants and also were less likely to orient with a head turn to a novel auditory stimulus (bell ring) when awake.

Conclusions

PNE may impair auditory sensory gating, which may contribute to disrupted sleep and to reduced auditory discrimination and learning, attention re-orienting, and/or arousal during wakefulness reported in other studies.

Implications

Links between PNE and reduced K-complex amplitude and delta power may represent altered cholinergic and GABAergic synaptic programming and possibly reflect early neural bases for PNE-linked disruptions in sleep quality and auditory processing. These may pose significant disadvantage for language acquisition, attention, and social interaction necessary for academic and social success.

Perirhinal circuits for memory processing

The perirhinal cortex (PRC) serves as the gateway to the hippocampus for episodic memory formation and plays a part in retrieval through its backward connectivity to various neocortical areas. First, I present the evidence suggesting that PRC neurons encode both experientially acquired object features and their associative relations. Recent studies have revealed circuit mechanisms in the PRC for the retrieval of cue-associated information, and have demonstrated that, in monkeys, PRC neuron-encoded information can be behaviourally read out. These studies, among others, support the theory that the PRC converts visual representations of an object into those of its associated features and initiates backward-propagating, interareal signalling for retrieval of nested associations of object features that, combined, extensionally represent the object meaning. I propose that the PRC works as the ventromedial hub of a 'two-hub model' at an apex of the hierarchy of a distributed memory network and integrates signals encoded in other downstream cortical areas that support diverse aspects of knowledge about an object.

The Emergence of Spindles and K-Complexes and the Role of the Dorsal Caudal Part of the Anterior Cingulate as the Generator of K-Complexes

The large multicomponent K-complex (KC) and the rhythmic spindle are the hallmarks of non-rapid eye movement (NREM)-2 sleep stage. We studied with magnetoencephalography (MEG) the progress of light sleep (NREM-1 and NREM-2) and emergence of KCs and spindles. Seven periods of interest (POI) were analyzed: wakefulness, the two quiet "core" periods of light sleep (periods free from any prominent phasic or oscillatory events) and four periods before and during spindles and KCs. For each POI, eight 2-s (1250 time slices) segments were used. We employed magnetic field tomography (MFT) to extract an independent tomographic estimate of brain activity from each MEG data sample. The spectral power was then computed for each voxel in the brain for each segment of each POI. The sets of eight maps from two POIs were contrasted using a voxel-by-voxel t-test. Only increased spectral power was identified in the four key contrasts between POIs before and during spindles and KCs versus the NREM2 core. Common increases were identified for all four subjects, especially within and close to the anterior cingulate cortex (ACC). These common increases were widespread for low frequencies, while for higher frequencies they were focal, confined to specific brain areas. For the pre-KC POI, only one prominent increase was identified, confined to the theta/alpha bands in a small area in the dorsal caudal part of ACC (dcACC). During KCs, the activity in this area grows in intensity and extent (in space and frequency), filling the space between the areas that expanded their low frequency activity (in the delta band) during NREM2 compared to NREM1. Our main finding is that prominent spectral power increases before NREM2 graphoelements are confined to the dcACC, and only for KCs, sharing common features with changes of activity in dcACC of the well-studied error related negativity (ERN). ERN is seen in awake state, in perceptual conflict and situations where there is a difference between expected and actual environmental or internal events. These results suggest that a KC is the sleep side of the awake state ERN, both serving their putative sentinel roles in the frame of the saliency network.

The heritability of the human K-complex: a twin study

Sleep electroencephalogram (EEG) has a trait-like nature. Several findings highlighted the heritability of spectral power in specific frequency ranges and sleep spindles during nonrapid eye movement (NREM) sleep. However, a genetic influence on the K-complex (KC), one of the electrophysiological hallmarks of NREM sleep, has never been assessed. Here, we investigated the heritability of the KC detected during NREM stage 2 comparing 10 monozygotic (MZ) and 10 dizygotic (DZ) twin pairs. Genetic variance analysis (GVA) and intraclass correlation coefficients (ICCs) were performed to assess the genetic effect and within-pair similarity for KC density, amplitude, and for the area under the curve (AUC) of the KC average waveform at Fz, Cz, and Pz scalp locations. Moreover, cluster analysis was performed on the KC average waveform profile. We observed a significant genetic effect on KC AUC at Cz and Pz, and on amplitude at Pz. Within-pair similarity (ICCs) was always significant for MZ twins except for KC density at Fz, whereas DZ twins always exhibited ICCs below the significance threshold, with the exception of density at Pz. The largest differences in within-pair similarity between MZ and DZ groups were observed again for AUC at Cz and Pz. MZ pairs accurately clustered for the KC average waveform with a higher frequency (successful clustering rate for MZ pairs: Fz = 60%; Cz = 80%; Pz = 90%) compared with DZ pairs (successful clustering rate for DZ pairs: Fz = 10%; Cz = 10%; Pz = none). Our results suggest the existence of a genetic influence on the human KC, particularly related to its morphology and maximally observable in central and parietal locations.

NREM sleep in the rodent neocortex and hippocampus reflects excitable dynamics

During non-rapid eye movement (NREM) sleep, neuronal populations in the mammalian forebrain alternate between periods of spiking and inactivity. Termed the slow oscillation in the neocortex and sharp wave-ripples in the hippocampus, these alternations are often considered separately but are both crucial for NREM functions. By directly comparing experimental observations of naturally-sleeping rats with a mean field model of an adapting, recurrent neuronal population, we find that the neocortical alternations reflect a dynamical regime in which a stable active state is interrupted by transient inactive states (slow waves) while the hippocampal alternations reflect a stable inactive state interrupted by transient active states (sharp waves). We propose that during NREM sleep in the rodent, hippocampal and neocortical populations are excitable: each in a stable state from which internal fluctuations or external perturbation can evoke the stereotyped population events that mediate NREM functions.

A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations

BACKGROUND

The Perturbational Complexity Index (PCI) was recently introduced to assess the capacity of thalamocortical circuits to engage in complex patterns of causal interactions. While showing high accuracy in detecting consciousness in brain-injured patients, PCI depends on elaborate experimental setups and offline processing, and has restricted applicability to other types of brain signals beyond transcranial magnetic stimulation and high-density EEG (TMS/hd-EEG) recordings.

OBJECTIVE

We aim to address these limitations by introducing PCI^ST, a fast method for estimating perturbational complexity of any given brain response signal.

METHODS

PCI^ST is based on dimensionality reduction and state transitions (ST) quantification of evoked potentials. The index was validated on a large dataset of TMS/hd-EEG recordings obtained from 108 healthy subjects and 108 brain-injured patients, and tested on sparse intracranial recordings (SEEG) of 9 patients undergoing intracranial single-pulse electrical stimulation (SPES) during wakefulness and sleep.

RESULTS

When calculated on TMS/hd-EEG potentials, PCI^ST performed with the same accuracy as the original PCI, while improving on the previous method by being computed in less than a second and requiring a simpler set-up. In SPES/SEEG signals, the index was able to quantify a systematic reduction of intracranial complexity during sleep, confirming the occurrence of state-dependent changes in the effective connectivity of thalamocortical circuits, as originally assessed through TMS/hd-EEG.

CONCLUSIONS

PCI^ST represents a fundamental advancement towards the implementation of a reliable and fast clinical tool for the bedside assessment of consciousness as well as a general measure to explore the neuronal mechanisms of loss/recovery of brain complexity across scales and models.

Improvement of Parasomnias After Treatment of Restless Leg Syndrome/ Periodic Limb Movement Disorder in Children

STUDY OBJECTIVES

Previous studies have shown that non-rapid eye movement (NREM) sleep parasomnias commonly coexist with restless legs syndrome (RLS) and periodic limb movement disorder (PLMD) in children, leading to speculation that RLS/PLMD may precipitate or worsen parasomnias. However, there are limited data about the effect of the treatment of RLS/PLMD on parasomnias in children. Hence, we performed this study to determine whether the treatment of RLS/PLMD with oral iron therapy is associated with improvement of parasomnias in children.

METHODS

A retrospective database was created for children with RLS/PLMD who were treated with iron therapy. These participants were followed for at least 1 year at Cincinnati Children's Hospital Medical Center. All participants had ferritin level testing and were treated with iron therapy. In addition, all participants underwent polysomnography before starting iron therapy for RLS/PLMD except for one participant who was already on iron but required a higher dose. Most participants underwent polysomnography after iron therapy.

RESULTS

A total of 226 participants were identified with the diagnosis of RLS/PLMD. Of these, 50 had parasomnias and 30 of them were treated with iron therapy. Of the 30 participants, RLS symptoms improved in 15 participants (50%) and resolution of parasomnias was noted in 12 participants (40%) participants after iron therapy. Repeat polysomnography after iron therapy was performed in 21 participants (70%). After iron therapy, there was a significant decrease in periodic limb movement index (17.2 ± 8.8 [before] versus 6.7 ± 7.3 [after] events/h, P < .001). In addition, there were significant decreases in PLMS (24.52 ± 9.42 [before] versus 7.50 ± 7.18 [after] events/h, P < .0001), PLMS-related arousals (4.71 ± 1.81 [before] versus 1.35 ± 1.43 [after] events/h, P < .0001), and total arousals (11.65 ± 5.49 [before] versus 8.94 ± 3.65 [after] events/h, P < .01) after iron therapy.

CONCLUSIONS

Parasomnias are common in our cohort of children with RLS/PLMD. Iron therapy was associated with a significant improvement in periodic limb movement index, RLS symptoms, and resolution of a significant proportion of NREM sleep parasomnias, suggesting that RLS/PLMD may precipitate NREM sleep parasomnia.

Remembering to Forget: A Dual Role for Sleep Oscillations in Memory Consolidation and Forgetting

It has been known since the time of patient H. M. and Karl Lashley's equipotentiality studies that the hippocampus and cortex serve mnestic functions. Current memory models maintain that these two brain structures accomplish unique, but interactive, memory functions. Specifically, most modeling suggests that memories are rapidly acquired during waking experience by the hippocampus, before being later consolidated into the cortex for long-term storage. Sleep has been shown to be critical for the transfer and consolidation of memories in the cortex. Like memory consolidation, a role for sleep in adaptive forgetting has both historical precedent, as Francis Crick suggested in 1983 that sleep was for "reverse-learning," and recent empirical support. In this article I review the evidence indicating that the same brain activity involved in sleep replay associated memory consolidation is responsible for sleep-dependent forgetting. In reviewing the literature, it became clear that both a cellular mechanism for systems consolidation and an agreed upon general, as well as cellular, mechanism for sleep-dependent forgetting is seldom discussed or is lacking. I advocate here for a candidate cellular systems consolidation mechanism wherein changes in calcium kinetics and the activation of consolidative signaling cascades arise from the triple phase locking of non-rapid eye movement sleep (NREMS) slow oscillation, sleep spindle and sharp-wave ripple rhythms. I go on to speculatively consider several sleep stage specific forgetting mechanisms and conclude by discussing a notional function of NREM-rapid eye movement sleep (REMS) cycling. The discussed model argues that the cyclical organization of sleep functions to first lay down and edit and then stabilize and integrate engrams. All things considered, it is increasingly clear that hallmark sleep stage rhythms, including several NREMS oscillations and the REMS hippocampal theta rhythm, serve the dual function of enabling simultaneous memory consolidation and adaptive forgetting. Specifically, the same sleep rhythms that consolidate new memories, in the cortex and hippocampus, simultaneously organize the adaptive forgetting of older memories in these brain regions.