Optimizing detection and analysis of slow waves in sleep EEG

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16-hour fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Proof-of-concept evidence for high-density EEG investigation of sleep slow wave traveling in First-Episode Psychosis

Schizophrenia is thought to reflect aberrant connectivity within cortico-cortical and reentrant thalamo-cortical loops, which physiologically integrate and coordinate the function of multiple cortical and subcortical structures. Despite extensive research, reliable biomarkers of such "dys-connectivity" remain to be identified at the onset of psychosis, and before exposure to antipsychotic drugs. Because slow waves travel across the brain during sleep, they represent an ideal paradigm to study pathological conditions affecting brain connectivity. Here, we provide proof-of-concept evidence for a novel approach to investigate slow wave traveling properties in First-Episode Psychosis (FEP) with high-density electroencephalography (EEG). Whole-night sleep recordings of 5 drug-naïve FEP and 5 age- and gender-matched healthy control subjects were obtained with a 256-channel EEG system. One patient was re-recorded after 6 months and 3 years of continuous clozapine treatment. Slow wave detection and traveling properties were obtained with an open-source toolbox. Slow wave density and slow wave traveled distance (measured as the line of longest displacement) were significantly lower in patients (p < 0.05). In the patient who was tested longitudinally during effective clozapine treatment, slow wave density normalized, while traveling distance only partially recovered. These preliminary findings suggest that slow wave traveling could be employed in larger samples to detect cortical "dys-connectivity" at psychosis onset.

Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.

Auditory stimulation during REM sleep modulates REM electrophysiology and cognitive performance

REM sleep is critical for memory, emotion, and cognition. Manipulating brain activity during REM could improve our understanding of its function and benefits. Earlier studies have suggested that auditory stimulation in REM might modulate REM time and reduce rapid eye movement density. Building on this, we studied the cognitive effects and electroencephalographic responses related to such stimulation. We used acoustic stimulation locked to eye movements during REM and compared two overnight conditions (stimulation and no-stimulation). We evaluated the impact of this stimulation on REM sleep duration and electrophysiology, as well as two REM-sensitive memory tasks: visual discrimination and mirror tracing. Our results show that this auditory stimulation in REM decreases the rapid eye movements that characterize REM sleep and improves performance on the visual task but is detrimental to the mirror tracing task. We also observed increased beta-band activity and decreased theta-band activity following stimulation. Interestingly, these spectral changes were associated with changes in behavioural performance. These results show that acoustic stimulation can modulate REM sleep and suggest that different memory processes underpin its divergent impacts on cognitive performance.

Improving the detection of sleep slow oscillations in electroencephalographic data

Study objectives

We aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection.

Method

SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set.

Results

Our custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time.

Conclusions

Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.

Maturation-dependent changes in cortical and thalamic activity during sleep slow waves: Insights from a combined EEG-fMRI study

INTRODUCTION

Studies using scalp EEG have shown that slow waves (0.5-4 Hz), the most prominent hallmark of NREM sleep, undergo relevant changes from childhood to adulthood, mirroring brain structural modifications and the acquisition of cognitive skills. Here we used simultaneous EEG-fMRI to investigate the cortical and subcortical correlates of slow waves in school-age children and determine their relative developmental changes.

METHODS

We analyzed data from 14 school-age children with self-limited focal epilepsy of childhood who fell asleep during EEG-fMRI recordings. Brain regions associated with slow-wave occurrence were identified using a voxel-wise regression that also modelled interictal epileptic discharges and sleep spindles. At the group level, a mixed-effects linear model was used. The results were qualitatively compared with those obtained from 2 adolescents with epilepsy and 17 healthy adults.

RESULTS

Slow waves were associated with hemodynamic-signal decreases in bilateral somatomotor areas. Such changes extended more posteriorly relative to those in adults. Moreover, the involvement of areas belonging to the default mode network changes as a function of age. No significant hemodynamic responses were observed in subcortical structures. However, we identified a significant correlation between age and thalamic hemodynamic changes.

CONCLUSIONS

Present findings indicate that the somatomotor cortex may have a key role in slow-wave expression throughout the lifespan. At the same time, they are consistent with a posterior-to-anterior shift in slow-wave distribution mirroring brain maturational changes. Finally, our results suggest that slow-wave changes may not reflect only neocortical modifications but also the maturation of subcortical structures, including the thalamus.

Altered Sleep Oscillations as Neurophysiological Biomarkers of Schizophrenia

Sleep spindles and slow waves are the two main oscillatory activities occurring during nonrapid eye movement (NREM) sleep. Here, we will first describe the electrophysiological characteristics of these sleep oscillations along with the neurophysiological and molecular mechanisms underlying their generation and synchronization in the healthy brain. We will then review the extant evidence of deficits in sleep spindles and, to a lesser extent, slow waves, including in slow wave-spindle coupling, in patients with Schizophrenia (SCZ) across the course of the disorder, from at-risk to chronic stages. Next, we will discuss how these sleep oscillatory deficits point to defects in neuronal circuits within the thalamocortical network as well as to alterations in molecular neurotransmission implicating the GABAergic and glutamatergic systems in SCZ. Finally, after explaining how spindle and slow waves may represent neurophysiological biomarkers with predictive, diagnostic, and prognostic potential, we will present novel pharmacological and neuromodulatory interventions aimed at restoring sleep oscillatory deficits in SCZ, which in turn may serve as target engagement biomarkers to ameliorate the clinical symptoms and the quality of life of individuals affected by this devastating brain disorder.

Origin, synchronization, and propagation of sleep slow waves in children

STUDY OBJECTIVES

Sleep slow wave activity, as measured using EEG delta power (<4 Hz), undergoes significant changes throughout development, mirroring changes in brain function and anatomy. Yet, age-dependent variations in the characteristics of individual slow waves have not been thoroughly investigated. Here we aimed at characterizing individual slow wave properties such as origin, synchronization, and cortical propagation at the transition between childhood and adulthood.

METHODS

We analyzed overnight high-density (256 electrodes) EEG recordings of healthy typically developing children (N=21, 10.3±1.5 years old) and young healthy adults (N=18, 31.1±4.4 years old). All recordings were preprocessed to reduce artifacts, and NREM slow waves were detected and characterized using validated algorithms. The threshold for statistical significance was set at p=0.05.

RESULTS

The slow waves of children were larger and steeper, but less widespread than those of adults. Moreover, they tended to mainly originate from and spread over more posterior brain areas. Relative to those of adults, the slow waves of children also displayed a tendency to more strongly involve and originate from the right than the left hemisphere. The separate analysis of slow waves characterized by high and low synchronization efficiency showed that these waves undergo partially distinct maturation patterns, consistent with their possible dependence on different generation and synchronization mechanisms.

CONCLUSIONS

Changes in slow wave origin, synchronization, and propagation at the transition between childhood and adulthood are consistent with known modifications in cortico-cortical and subcortico-cortical brain connectivity. In this light, changes in slow-wave properties may provide a valuable yardstick to assess, track, and interpret physiological and pathological development.

Detection of neuronal OFF periods as low amplitude neural activity segments

BACKGROUND

During non-rapid eye movement sleep (NREM), alternating periods of synchronised high (ON period) and low (OFF period) neuronal activity are associated with high amplitude delta band (0.5-4 Hz) oscillations in neocortical electrophysiological signals termed slow waves. As this oscillation is dependent crucially on hyperpolarisation of cortical cells, there is an interest in understanding how neuronal silencing during OFF periods leads to the generation of slow waves and whether this relationship changes between cortical layers. A formal, widely adopted definition of OFF periods is absent, complicating their detection. Here, we grouped segments of high frequency neural activity containing spikes, recorded as multiunit activity from the neocortex of freely behaving mice, on the basis of amplitude and asked whether the population of low amplitude (LA) segments displayed the expected characteristics of OFF periods.

RESULTS

Average LA segment length was comparable to previous reports for OFF periods but varied considerably, from as short as 8 ms to > 1 s. LA segments were longer and occurred more frequently in NREM but shorter LA segments also occurred in half of rapid eye movement sleep (REM) epochs and occasionally during wakefulness. LA segments in all states were associated with a local field potential (LFP) slow wave that increased in amplitude with LA segment duration. We found that LA segments > 50 ms displayed a homeostatic rebound in incidence following sleep deprivation whereas short LA segments (< 50 ms) did not. The temporal organisation of LA segments was more coherent between channels located at a similar cortical depth.

CONCLUSION

We corroborate previous studies showing neural activity signals contain uniquely identifiable periods of low amplitude with distinct characteristics from the surrounding signal known as OFF periods and attribute the new characteristics of vigilance-state-dependent duration and duration-dependent homeostatic response to this phenomenon. This suggests that ON/OFF periods are currently underdefined and that their appearance is less binary than previously considered, instead representing a continuum.

Coupling between the prelimbic cortex, nucleus reuniens, and hippocampus during NREM sleep remains stable under cognitive and homeostatic demands

The interplay between the medial prefrontal cortex and hippocampus during non-rapid eye movement (NREM) sleep contributes to the consolidation of contextual memories. To assess the role of the thalamic nucleus reuniens (Nre) in this interaction, we investigated the coupling of neuro-oscillatory activities among prelimbic cortex, Nre, and hippocampus across sleep states and their role in the consolidation of contextual memories using multi-site electrophysiological recordings and optogenetic manipulations. We showed that ripples are time-locked to the Up state of cortical slow waves, the transition from UP to DOWN state in thalamic slow waves, the troughs of cortical spindles, and the peaks of thalamic spindles during spontaneous sleep, rebound sleep and sleep following a fear conditioning task. In addition, spiking activity in Nre increased before hippocampal ripples, and the phase-locking of hippocampal ripples and thalamic spindles during NREM sleep was stronger after acquisition of a fear memory. We showed that optogenetic inhibition of Nre neurons reduced phase-locking of ripples to cortical slow waves in the ventral hippocampus whilst their activation altered the preferred phase of ripples to slow waves in ventral and dorsal hippocampi. However, none of these optogenetic manipulations of Nre during sleep after acquisition of fear conditioning did alter sleep-dependent memory consolidation. Collectively, these results showed that Nre is central in modulating hippocampus and cortical rhythms during NREM sleep.

Closed-loop modulation of local slow oscillations in human NREM sleep

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.

Individual slow wave events give rise to macroscopic fMRI signatures and drive the strength of the BOLD signal in human resting-state EEG-fMRI recordings

The slow wave state is a general state of quiescence interrupted by sudden bursts of activity or so-called slow wave events (SWEs). Recently, the relationship between SWEs and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals was assessed in rodent models which revealed cortex-wide BOLD activation. However, it remains unclear which macroscopic signature corresponds to these specific neurophysiological events in the human brain. Therefore, we analyzed simultaneous electroencephalographic (EEG)-fMRI data during human non-REM sleep. SWEs individually detected in the EEG data were used as predictors in event-related fMRI analyses to examine the relationship between SWEs and fMRI signals. For all 10 subjects we identified significant changes in BOLD activity associated with SWEs covering substantial parts of the gray matter. As demonstrated in rodents, we observed a direct relation of a neurophysiological event to specific BOLD activation patterns. We found a correlation between the number of SWEs and the spatial extent of these BOLD activation patterns and discovered that the amplitude of the BOLD response strongly depends on the SWE amplitude. As altered SWE propagation has recently been found in neuropsychiatric diseases, it is critical to reveal the brain's physiological slow wave state networks to potentially establish early imaging biomarkers for various diseases long before disease onset.

Local sleep: A new concept in brain plasticity

Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.

Schizophrenia-associated variation at ZNF804A correlates with altered experience-dependent dynamics of sleep slow waves and spindles in healthy young adults

The rs1344706 polymorphism in ZNF804A is robustly associated with schizophrenia and schizophrenia is, in turn, associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. We recruited healthy adult males with no history of psychiatric disorder from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the schizophrenia-associated 'A' allele (N = 22) or the alternative 'C' allele (N = 18) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequence task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. Average MST learning and sleep-dependent performance improvements were similar across genotype groups, albeit more variable in the AA group. During sleep after learning, CC participants showed increased slow-wave (SW) and spindle amplitudes, plus augmented coupling of SW activity across recording electrodes. SW and spindles in those with the AA genotype were insensitive to learning, whilst SW coherence decreased following MST training. Accordingly, NREM neurophysiology robustly predicted the degree of overnight motor memory consolidation in CC carriers, but not in AA carriers. We describe evidence that rs1344706 polymorphism in ZNF804A is associated with changes in the coordinated neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of schizophrenia-associated common genetic variants on neural circuit oscillations and function.

Exposure to relaxing words during sleep promotes slow-wave sleep and subjective sleep quality

Our thoughts alter our sleep, but the underlying mechanisms are still unknown. We propose that mental processes are active to a greater or lesser extent during sleep and that this degree of activation affects our sleep depth. We examined this notion by activating the concept of "relaxation" during sleep using relaxation-related words in 50 healthy participants. In support of our hypothesis, playing relaxing words during non-rapid eye movement sleep extended the time spent in slow-wave sleep, increased power in the slow-wave activity band after the word cue, and abolished an asymmetrical sleep depth during the word presentation period. In addition, participants reported a higher sleep quality and elevated subjective alertness. Our results support the notion that the activation of mental concepts during sleep can influence sleep depth. They provide a basis for interventions using targeted activations to promote sleep depth and sleep quality to foster well-being and health.

Sleep Abnormalities in Schizophrenia: State of the Art and Next Steps

Sleep disturbances are commonly observed in schizophrenia, including in chronic, early-course, and first-episode patients. This has generated considerable interest, both in clinical and research endeavors, in characterizing the relationship between disturbed sleep and schizophrenia. Sleep features can be objectively assessed with EEG recordings. Traditionally, EEG studies have focused on sleep architecture, which includes non-REM and REM sleep stages. More recently, numerous studies have investigated alterations in sleep-specific rhythms, including EEG oscillations, such as sleep spindles and slow waves, in individuals with schizophrenia compared with control subjects. In this article, the author reviews state-of-the-art evidence of disturbed sleep in schizophrenia, starting from the relationship between sleep disturbances and clinical symptoms. First, the author presents studies demonstrating abnormalities in sleep architecture and sleep-oscillatory rhythms in schizophrenia and related psychotic disorders, with an emphasis on recent work demonstrating sleep spindles and slow-wave deficits in early-course and first-episode schizophrenia. Next, the author shows how these sleep abnormalities relate to the cognitive impairments in patients diagnosed with schizophrenia and point to dysfunctions in underlying thalamocortical circuits, Ca+ channel activity, and GABA-glutamate neurotransmission. Finally, the author discusses some of the next steps needed to further establish the role of altered sleep in schizophrenia, including the need to investigate sleep abnormalities across the psychotic spectrum and to establish their relationship with circadian disturbances, which in turn will contribute to the development of novel sleep-informed treatment interventions.

Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep

The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography (hd-EEG) in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out to different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.

Extended Visual Sequence Learning Leaves a Local Trace in the Spontaneous EEG

We have previously demonstrated that, in rested subjects, extensive practice in a motor learning task increased both electroencephalographic (EEG) theta power in the areas involved in learning and improved the error rate in a motor test that shared similarities with the task. A nap normalized both EEG and performance changes. We now ascertain whether extensive visual declarative learning produces results similar to motor learning. Thus, during the morning, we recorded high-density EEG in well rested young healthy subjects that learned the order of different visual sequence task (VSEQ) for three one-hour blocks. Afterward, a group of subjects took a nap and another rested quietly. Between each VSEQ block, we recorded spontaneous EEG (sEEG) at rest and assessed performance in a motor test and a visual working memory test that shares similarities with VSEQ. We found that after the third block, VSEQ induced local theta power increases in the sEEG over a right temporo-parietal area that was engaged during the task. This local theta increase was preceded by increases in alpha and beta power over the same area and was paralleled by performance decline in the visual working memory test. Only after the nap, VSEQ learning rate improved and performance in the visual working memory test was restored, together with partial normalization of the local sEEG changes. These results suggest that intensive learning, like motor learning, produces local theta power increases, possibly reflecting local neuronal fatigue. Sleep may be necessary to resolve neuronal fatigue and its effects on learning and performance.

Role of corpus callosum in sleep spindle synchronization and coupling with slow waves

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.

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.

Neural fatigue due to intensive learning is reversed by a nap but not by quiet waking

Do brain circuits become fatigued due to intensive neural activity or plasticity? Is sleep necessary for recovery? Well-rested subjects trained extensively in a visuo-motor rotation learning task (ROT) or a visuo-motor task without rotation learning (MOT), followed by sleep or quiet wake. High-density electroencephalography showed that ROT training led to broad increases in EEG power over a frontal cluster of electrodes, with peaks in the theta (mean ± SE: 24% ± 6%, p = 0.0013) and beta ranges (10% ± 3%, p = 0.01). These traces persisted in the spontaneous EEG (sEEG) between sessions (theta: 42% ± 8%, p = 0.0001; beta: 35% ± 7%, p = 0.002) and were accompanied by increased errors in a motor test with kinematic characteristics and neural substrates similar to ROT (81.8% ± 0.8% vs. 68.2% ± 2.3%; two-tailed paired t-test: p = 0.00001; Cohen's d = 1.58), as well as by score increases of subjective task-specific fatigue (4.00 ± 0.39 vs. 5.36 ± 0.39; p = 0.0007; Cohen's d = 0.60). Intensive practice with MOT did not affect theta sEEG or the motor test. A nap, but not quiet wake, induced a local sEEG decrease of theta power by 33% (SE: 8%, p = 0.02), renormalized test performance (70.9% ± 2.9% vs 79.1% ± 2.7%, p = 0.018, Cohen's d = 0.85), and improved learning ability in ROT (adaptation rate: 71.2 ± 1.2 vs. 73.4 ± 0.9, p = 0.024; Cohen's d = 0.60). Thus, sleep is necessary to restore plasticity-induced fatigue and performance.

Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: a prospective observational study of delirium in elderly cardiac surgical patients

INTRODUCTION

Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome.

METHODS AND ANALYSIS

P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time.

ETHICS AND DISSEMINATION

P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media.

TRIAL REGISTRATION NUMBER

NCT03291626.

Slow Waves Promote Sleep-Dependent Plasticity and Functional Recovery after Stroke

Functional recovery after stroke is associated with a remapping of neural circuits. This reorganization is often associated with low-frequency, high-amplitude oscillations in the peri-infarct zone in both rodents and humans. These oscillations are reminiscent of sleep slow waves (SW) and suggestive of a role for sleep in brain plasticity that occur during stroke recovery; however, direct evidence is missing. Using a stroke model in male mice, we showed that stroke was followed by a transient increase in NREM sleep accompanied by reduced amplitude and slope of ipsilateral NREM sleep SW. We next used 5 ms optical activation of Channelrhodopsin 2-expressing pyramidal neurons, or 200 ms silencing of Archeorhodopsin T-expressing pyramidal neurons, to generate local cortical UP, or DOWN, states, respectively, both sharing similarities with spontaneous NREM SW in freely moving mice. Importantly, we found that single optogenetically evoked SW (SWopto) in the peri-infarct zone, randomly distributed during sleep, significantly improved fine motor movements of the limb corresponding to the sensorimotor stroke lesion site compared with spontaneous recovery and control conditions, while motor strength remained unchanged. In contrast, SWopto during wakefulness had no effect. Furthermore, chronic SWopto during sleep were associated with local axonal sprouting as revealed by the increase of anatomic presynaptic and postsynaptic markers in the peri-infarct zone and corresponding contralesional areas to cortical circuit reorganization during stroke recovery. These results support a role for sleep SW in cortical circuit plasticity and sensorimotor recovery after stroke and provide a clinically relevant framework for rehabilitation strategies using neuromodulation during sleep.SIGNIFICANCE STATEMENT Brain stroke is one of the leading causes of death and major disabilities in the elderly worldwide. A better understanding of the pathophysiological mechanisms underlying spontaneous brain plasticity after stroke, together with an optimization of rehabilitative strategies, are essential to improve stroke treatments. Here, we investigate the role of optogenetically induced sleep slow waves in an animal model of ischemic stroke and identify sleep as a window for poststroke intervention that promotes neuroplasticity and facilitates sensorimotor recovery.

Budgerigars have complex sleep structure similar to that of mammals

Birds and mammals share specialized forms of sleep including slow wave sleep (SWS) and rapid eye movement sleep (REM), raising the question of why and how specialized sleep evolved. Extensive prior studies concluded that avian sleep lacked many features characteristic of mammalian sleep, and therefore that specialized sleep must have evolved independently in birds and mammals. This has been challenged by evidence of more complex sleep in multiple songbird species. To extend this analysis beyond songbirds, we examined a species of parrot, the sister taxon to songbirds. We implanted adult budgerigars (Melopsittacus undulatus) with electroencephalogram (EEG) and electrooculogram (EOG) electrodes to evaluate sleep architecture, and video monitored birds during sleep. Sleep was scored with manual and automated techniques, including automated detection of slow waves and eye movements. This can help define a new standard for how to score sleep in birds. Budgerigars exhibited consolidated sleep, a pattern also observed in songbirds, and many mammalian species, including humans. We found that REM constituted 26.5% of total sleep, comparable to humans and an order of magnitude greater than previously reported. Although we observed no spindles, we found a clear state of intermediate sleep (IS) similar to non-REM (NREM) stage 2. Across the night, SWS decreased and REM increased, as observed in mammals and songbirds. Slow wave activity (SWA) fluctuated with a 29-min ultradian rhythm, indicating a tendency to move systematically through sleep states as observed in other species with consolidated sleep. These results are at variance with numerous older sleep studies, including for budgerigars. Here, we demonstrated that lighting conditions used in the prior budgerigar study-and commonly used in older bird studies-dramatically disrupted budgerigar sleep structure, explaining the prior results. Thus, it is likely that more complex sleep has been overlooked in a broad range of bird species. The similarities in sleep architecture observed in mammals, songbirds, and now budgerigars, alongside recent work in reptiles and basal birds, provide support for the hypothesis that a common amniote ancestor possessed the precursors that gave rise to REM and SWS at one or more loci in the parallel evolution of sleep in higher vertebrates. We discuss this hypothesis in terms of the common plan of forebrain organization shared by reptiles, birds, and mammals.

Decreased Evoked Slow-Activity After tDCS in Disorders of Consciousness

Due to life-saving medical advances, the diagnosis and treatment of disorders of consciousness (DOC) has become a more commonly occurring clinical issue. One recently developed intervention option has been non-invasive transcranial direct current stimulation. This dichotomy of patient responders may be better understood by investigating the mechanism behind the transcranial direct current stimulation (tDCS) intervention. The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) has been an important diagnostic tool in DOC patients. We therefore examined the neural response using TMS-EEG both before and after tDCS in seven DOC patients (four diagnosed as in a minimally conscious state and three with unresponsive wakefulness syndrome). tDCS was applied over the dorsolateral prefrontal cortex, while TMS pulses were applied to the premotor cortex. None of the seven patients showed relevant behavioral change after tDCS. We did, however, find that the overall evoked slow activity was reduced following tDCS intervention. We also found a positive correlation between the strength of the slow activity and the amount of high-frequency suppression. However, there was no significant pre-post tDCS difference in high frequencies. In the resting-state EEG, we observed that both the incidence of slow waves and the positive slope of the wave were affected by tDCS. Taken together, these results suggest that the tDCS intervention can reduce the slow-wave activity component of bistability, but this may not directly affect high-frequency activity. We hypothesize that while reduced slow activity may be necessary for the recovery of neural function, especially consciousness, this alone is insufficient.

Prior Practice Affects Movement-Related Beta Modulation and Quiet Wake Restores It to Baseline

Beta oscillations (13.5−25 Hz) over the sensorimotor areas are characterized by a power decrease during movement execution (event-related desynchronization, ERD) and a sharp rebound after the movement end (event-related synchronization, ERS). In previous studies, we demonstrated that movement-related beta modulation depth (peak ERS-ERD) during reaching increases within 1-h practice. This increase may represent plasticity processes within the sensorimotor network. If so, beta modulation during a reaching test should be affected by previous learning activity that engages the sensorimotor system but not by learning involving other systems. We thus recorded high-density EEG activity in a group of healthy subjects performing three 45-min blocks of motor adaptation task to a visually rotated display (ROT) and in another performing three blocks of visual sequence-learning (VSEQ). Each block of either ROT or VSEQ was followed by a simple reaching test (mov) without rotation. We found that beta modulation depth increased with practice across mov tests. However, such an increase was greater in the group performing ROT over both the left and frontal areas previously involved in ROT. Importantly, beta modulation values returned to baseline values after a 90-min of either nap or quiet wake. These results show that previous practice leaves a trace in movement-related beta modulation and therefore such increases are cumulative. Furthermore, as sleep is not necessary to bring beta modulation values to baseline, they could reflect local increases of neuronal activity and decrease of energy and supplies.

Sleep spindle and slow wave abnormalities in schizophrenia and other psychotic disorders: Recent findings and future directions

Sleep spindles and slow waves are the two main oscillatory activities occurring during NREM sleep. Slow waves are ∼1 Hz, high amplitude, negative-positive deflections that are primarily generated and coordinated within the cortex, whereas sleep spindles are 12-16 Hz, waxing and waning oscillations that are initiated within the thalamus and regulated by thalamo-cortical circuits. In healthy subjects, these oscillations are thought to be responsible for the restorative aspects of sleep and have been increasingly shown to be involved in learning, memory and plasticity. Furthermore, deficits in sleep spindles and, to lesser extent, slow waves have been reported in both chronic schizophrenia (SCZ) and early course psychosis patients. In this article, we will first describe sleep spindle and slow wave characteristics, including their putative functional roles in the healthy brain. We will then review electrophysiological, genetic, and cognitive studies demonstrating spindle and slow wave impairments in SCZ and other psychotic disorders, with particularly emphasis on recent findings in early course patients. Finally, we will discuss how future work, including sleep studies in individuals at clinical high risk for psychosis, may help position spindles and slow waves as candidate biomarkers, as well as novel treatment targets, for SCZ and related psychotic disorders.

Slow wave oscillations in Schizophrenia First-Degree Relatives: A confirmatory analysis and feasibility study on slow wave traveling

Abnormal sleep oscillations have recently been proposed as endophenotypes of schizophrenia. However, optimization of methodological approaches is still necessary to standardize analyses of their microstructural characteristics. Additionally, some relevant features of these oscillations remain unexplored in pathological conditions. Among others, slow wave traveling is a promising proxy for diurnal processes of brain connectivity and excitability. The study of slow oscillations propagation appears particularly relevant when schizophrenia is conceptualized as a dys-connectivity syndrome. Given the rising knowledge on the neurobiological mechanisms underlying slow wave traveling, this measure might offer substantial advantages over other approaches in investigating brain connectivity. Herein we: 1) confirm the stability of our previous findings on slow waves and sleep spindles in FDRs using different automated algorithms, and 2) report the dynamics of slow wave traveling in FDRs of Schizophrenia patients. A 256-channel, high-density EEG system was employed to record a whole night of sleep of 16 FDRs and 16 age- and gender-matched control subjects. A recently developed, open source toolbox was used for slow wave visualization and detection. Slow waves were confirmed to be significantly smaller in FDRs compared to the control group. Additionally, several traveling parameters were analyzed. Traveled distances were found to be significantly reduced in FDRs, whereas origins showed a different topographical pattern of distribution from control subjects. In contrast, local speed did not differ between groups. Overall, these results suggest that slow wave traveling might be a viable method to study pathological conditions interfering with brain connectivity.

Integrity of Corpus Callosum Is Essential for theCross-Hemispheric Propagation of Sleep Slow Waves:A High-Density EEG Study in Split-Brain Patients

The slow waves of non-rapid eye movement (NREM) sleep reflect experience-dependent plasticity and play a direct role in the restorative functions of sleep. Importantly, slow waves behave as traveling waves, and their propagation is assumed to occur through cortico-cortical white matter connections. In this light, the corpus callosum (CC) may represent the main responsible for cross-hemispheric slow-wave propagation. To verify this hypothesis, we performed overnight high-density (hd)-EEG recordings in five patients who underwent total callosotomy due to drug-resistant epilepsy (CPs; two females), in three noncallosotomized neurologic patients (NPs; two females), and in a sample of 24 healthy adult subjects (HSs; 13 females). In all CPs slow waves displayed a significantly reduced probability of cross-hemispheric propagation and a stronger inter-hemispheric asymmetry. In both CPs and HSs, the incidence of large slow waves within individual NREM epochs tended to differ across hemispheres, with a relative overall predominance of the right over the left hemisphere. The absolute magnitude of this asymmetry was greater in CPs relative to HSs. However, the CC resection had no significant effects on the distribution of slow-wave origin probability across hemispheres. The present results indicate that CC integrity is essential for the cross-hemispheric traveling of slow waves in human sleep, which is in line with the assumption of a direct relationship between white matter integrity and slow-wave propagation. Our findings also revealed a residual cross-hemispheric slow-wave propagation that may rely on alternative pathways, including cortico-subcortico-cortical loops. Finally, these data indicate that the lack of the CC does not lead to differences in slow-wave generation across brain hemispheres.

SIGNIFICANCE STATEMENT The slow waves of NREM sleep behave as traveling waves, and their propagation has been suggested to reflect the integrity of white matter cortico-cortical connections. To directly assess this hypothesis, here we investigated the role of the corpus callosum in the cortical spreading of NREM slow waves through the study of a rare population of totally callosotomized patients. Our results demonstrate a causal role of the corpus callosum in the cross-hemispheric traveling of sleep slow waves. Additionally, we found that callosotomy does not affect the relative tendency of each hemisphere at generating slow waves. Incidentally, we also found that slow waves tend to originate more often in the right than in the left hemisphere in both callosotomized and healthy adult individuals.

Optimization of the Cortical Traveling Wave Analysis framework for feasibility in Stereo-Electroencephalography

The study of brain waves propagation is of interest to understand the neural involvement in both physiological and pathological events, such as interictal epileptic spikes (IES). The possibility to track the trajectory of IESs could be useful to better characterize the role of the involved structures in the epileptic network, adding valuable information to the epileptic focus localization. Methods for the cortical traveling wave analysis (CTWA) have been proposed to trace the preferred propagation path of sleep slow waves, using scalp high-density EEG and reconstructing the trajectories both in the sensors and in the sources space. In this work, we propose a feasibility study of the application of these concepts to Stereo-EEG (SEEG) data for the analysis of IES. Through simulations, we selected the best performing Electrical Source Imaging inverse solution for our purpose and illustrate the CTWA procedure. We further show an exemplary application on real data and discuss advantages and pitfalls of the application of CTWA in SEEG.

Characterizing Sleep Spindles in Sheep

Sleep spindles are distinctive transient patterns of brain activity that typically occur during non-rapid eye movement (NREM) sleep in humans and other mammals. Thought to be important for the consolidation of learning, they may also be useful for indicating the progression of aging and neurodegenerative diseases. The aim of this study was to characterize sleep spindles in sheep (Ovis aries). We recorded electroencephalographs wirelessly from six sheep over a continuous period containing 2 nights and a day. We detected and characterized spindles using an automated algorithm. We found that sheep sleep spindles fell within the classical range seen in humans (10–16 Hz), but we did not see a further separation into fast and slow bands. Spindles were detected predominantly during NREM sleep. Spindle characteristics (frequency, duration, density, topography) varied between individuals, but were similar within individuals between nights. Spindles that occurred during NREM sleep in daytime were indistinguishable from those found during NREM sleep at night. Surprisingly, we also detected numerous spindle-like events during unequivocal periods of wake during the day. These events were mainly local (detected at single sites), and their characteristics differed from spindles detected during sleep. These “wake spindles” are likely to be events that are commonly categorized as “spontaneous alpha activity” during wake. We speculate that wake and sleep spindles are generated via different mechanisms, and that wake spindles play a role in cognitive processes that occur during the daytime.

Understanding the interplay of sleep and aging: Methodological challenges

In quest of new avenues to explain, predict, and treat pathophysiological conditions during aging, research on sleep and aging has flourished. Despite the great scientific potential to pinpoint mechanistic pathways between sleep, aging, and pathology, only little attention has been paid to the suitability of analytic procedures applied to study these interrelations. On the basis of electrophysiological sleep and structural brain data of healthy younger and older adults, we identify, illustrate, and resolve methodological core challenges in the study of sleep and aging. We demonstrate potential biases in common analytic approaches when applied to older populations. We argue that uncovering age-dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age-independent interindividual differences. Age-adapted methodological approaches are thus required to foster the development of valid and reliable biomarkers of age-associated cognitive pathologies.

Local sleep-like events during wakefulness and their relationship to decreased alertness in astronauts on ISS

Adequate sleep quantity and quality is required to maintain vigilance, cognitive and learning processes. A decrease of sleep quantity preflight and on the International Space Station (ISS) has been reported. Recent counter-measures have been implemented to better regulate sleep opportunities on ISS. In our study, astronauts were allocated enough time for sleep the night before the recordings. However, for proper sleep recovery, the quality of sleep is also critical. Unfortunately, data on sleep quality have yet to be acquired from the ISS. Here, we investigate sleep pressure markers during wakefulness in five astronauts throughout their 6-month space mission by the mean of electroencephalographic recordings. We show a global increase of theta oscillations (5–7 Hz) on the ISS compared to on Earth before the mission. We also show that local sleep-like events, another marker of sleep pressure, are more global in space (p < 0.001). By analysing the performances of the astronauts during a docking simulation, we found that local sleep-like events are more global when reaction times are slower (R² = 0.03, p = 0.006) and there is an increase of reaction times above 244 ms after 2 months in space (p = 0.012). Our analyses provide first evidence for increased sleep pressure in space and raise awareness on possible impacts on visuomotor performances in space.

Regional Delta Waves In Human Rapid Eye Movement Sleep

Although the EEG slow wave of sleep is typically considered to be a hallmark of nonrapid eye movement (NREM) sleep, recent work in mice has shown that slow waves can also occur in REM sleep. Here, we investigated the presence and cortical distribution of negative delta (1-4 Hz) waves in human REM sleep by analyzing high-density EEG sleep recordings obtained in 28 healthy subjects. We identified two clusters of delta waves with distinctive properties: (1) a frontal-central cluster characterized by ∼2.5-3.0 Hz, relatively large, notched delta waves (so-called "sawtooth waves") that tended to occur in bursts, were associated with increased gamma activity and rapid eye movements (EMs), and upon source modeling displayed an occipital-temporal and a frontal-central component and (2) a medial-occipital cluster characterized by more isolated, slower (<2 Hz), and smaller waves that were not associated with rapid EMs, displayed a negative correlation with gamma activity, and were also found in NREM sleep. Therefore, delta waves are an integral part of REM sleep in humans and the two identified subtypes (sawtooth and medial-occipital slow waves) may reflect distinct generation mechanisms and functional roles. Sawtooth waves, which are exclusive to REM sleep, share many characteristics with ponto-geniculo-occipital waves described in animals and may represent the human equivalent or a closely related event, whereas medial-occipital slow waves appear similar to NREM sleep slow waves.SIGNIFICANCE STATEMENT The EEG slow wave is typically considered a hallmark of nonrapid eye movement (NREM) sleep, but recent work in mice has shown that it can also occur in REM sleep. By analyzing high-density EEG recordings collected in healthy adult individuals, we show that REM sleep is characterized by prominent delta waves also in humans. In particular, we identified two distinctive clusters of delta waves with different properties: a frontal-central cluster characterized by faster, activating "sawtooth waves" that share many characteristics with ponto-geniculo-occipital waves described in animals and a medial-occipital cluster containing slow waves that are more similar to NREM sleep slow waves. These findings indicate that REM sleep is a spatially and temporally heterogeneous state and may contribute to explaining its known functional and phenomenological properties.

Sleep as a model to understand neuroplasticity and recovery after stroke: Observational, perturbational and interventional approaches

Our own experiences with disturbances to sleep demonstrate its crucial role in the recovery of cognitive functions. This importance is likely enhanced in the recovery from stroke; both in terms of its physiology and cognitive abilities. Decades of experimental research have highlighted which aspects and mechanisms of sleep are likely to underlie these forms of recovery. Conversely, damage to certain areas of the brain, as well as the indirect effects of stroke, may disrupt sleep. However, only limited research has been conducted which seeks to directly explore this bidirectional link between both the macro and micro-architecture of sleep and stroke. Here we describe a series of semi-independent approaches that aim to establish this link through observational, perturbational, and interventional experiments. Our primary aim is to describe the methodology for future clinical and translational research needed to delineate competing accounts of the current data. At the observational level we suggest the use of high-density EEG recording, combined analysis of macro and micro-architecture of sleep, detailed analysis of the stroke lesion, and sensitive measures of functional recovery. The perturbational approach attempts to find the causal links between sleep and stroke. We promote the use of transcranial magnetic stimulation combined with EEG to examine the cortical dynamics of the peri-infarct stroke area. Translational research should take this a step further using optogenetic techniques targeting more specific cell populations. The interventional approach focuses on how the same clinical and translational perturbational techniques can be adapted to influence long-term recovery of function.

Individual spindle detection and analysis in high-density recordings across the night and in thalamic stroke

Sleep spindles are thalamocortical oscillations associated with several behavioural and clinical phenomena. In clinical populations, spindle activity has been shown to be reduced in schizophrenia, as well as after thalamic stroke. Automatic spindle detection algorithms present the only feasible way to systematically examine individual spindle characteristics. We took an established algorithm for spindle detection, and adapted it to high-density EEG sleep recordings. To illustrate the detection and analysis procedure, we examined how spindle characteristics changed across the night and introduced a linear mixed model approach applied to individual spindles in adults (n = 9). Next we examined spindle characteristics between a group of paramedian thalamic stroke patients (n = 9) and matched controls. We found a high spindle incidence rate and that, from early to late in the night, individual spindle power increased with the duration and globality of spindles; despite decreases in spindle incidence and peak-to-peak amplitude. In stroke patients, we found that only left-sided damage reduced individual spindle power. Furthermore, reduction was specific to posterior/fast spindles. Altogether, we demonstrate how state-of-the-art spindle detection techniques, applied to high-density recordings, and analysed using advanced statistical approaches can yield novel insights into how both normal and pathological circumstances affect sleep.

Slow wave detection in sleeping mice: Comparison of traditional and machine learning methods

BACKGROUND

During slow-wave sleep the electroencephalographic (EEG) and local field potential (LFP) recordings reveal the presence of large amplitude slow waves. Systematic extraction of individual slow waves is not trivial.

NEW METHOD

In this study, we used the neural network pattern recognition to detect individual slow waves in LFP recorded from mice as well as other commonly used methods that are based on fast frequencies modulation, amplitude, or duration.

RESULTS

The number and quality of events detected as slow waves depended on the chosen method of detection, level of thresholds, or on combination of methods. Each individual method yields some false-positive and false-negative detections. Typically, the fast frequency-method has a higher false discovery rate, but almost no missing waves; amplitude-based method has relatively high false-positive and false-negative rates; duration-based method has low false-negative rates; neural network pattern recognition approach has the lowest false-positive rate among individual methods, often rejecting waves that were falsely detected by other approaches. Combining all 4 detection methods practically eliminated false-positive errors, but a large number of slow waves remained undetected.

CONCLUSIONS

The use of a particular method of slow wave detection needs to be adjusted to the objectives of a given study: to detect all slow waves, but also numerous false positives can be achieved using the fast frequency approach. Neural network pattern recognition method alone can detect slow waves with the lowest false-positive rate, that can be further minimized with the use of combination of other methods.

Local and Widespread Slow Waves in Stable NREM Sleep: Evidence for Distinct Regulation Mechanisms

Previous work showed that two types of slow waves are temporally dissociated during the transition to sleep: widespread, large and steep slow waves predominate early in the falling asleep period (type I), while smaller, more circumscribed slow waves become more prevalent later (type II). Here, we studied the possible occurrence of these two types of slow waves in stable non-REM (NREM) sleep and explored potential differences in their regulation. A heuristic approach based on slow wave synchronization efficiency was developed and applied to high-density electroencephalographic (EEG) recordings collected during consolidated NREM sleep to identify the potential type I and type II slow waves. Slow waves with characteristics compatible with those previously described for type I and type II were identified in stable NREM sleep. Importantly, these slow waves underwent opposite changes across the night, with only type II slow waves displaying a clear homeostatic regulation. In addition, we showed that the occurrence of type I slow waves was often followed by larger type II slow waves, whereas the occurrence of type II slow waves was usually followed by smaller type I waves. Finally, type II slow waves were associated with a relative increase in spindle activity, while type I slow waves triggered periods of high-frequency activity. Our results provide evidence for the existence of two distinct slow wave synchronization processes that underlie two different types of slow waves. These slow waves may have different functional roles and mark partially distinct “micro-states” of the sleeping brain.

Sleep spindles and slow waves in schizophrenia and related disorders: main findings, challenges and future perspectives

Sleep abnormalities have recently gained renewed attention in patients diagnosed with schizophrenia. Disrupted thalamocortical brain oscillations hold promise as putative biomarkers or endophenotypes of the disorder. Despite an increase in studies related to sleep spindle and slow-wave activity, findings remain in part contradictory. Although sleep spindle deficits have been confirmed in several groups of patients with chronic, medicated schizophrenia, data on the early stages of the disorder and in unmedicated subjects are still insufficient. Findings on slow-wave abnormalities are largely inconclusive, possibly due to the different criteria employed to define the phenomenon and to the influence of atypical antipsychotics. In this review, we aim to address the methodological and practical issues that may have limited the consistency of findings across research groups and different patient populations. Given the neurobiological relevance of these oscillations, which reflect the integrity of thalamocortical and cortico-cortical function, research in this domain should be encouraged. To promote widespread consensus over the scientific and clinical implications of these sleep-related phenomena, we advocate uniform and sound methodological approaches. These should encompass electroencephalographic recording and analysis techniques but also selection criteria and characterization of clinical populations.

Deep sleep maintains learning efficiency of the human brain

It is hypothesized that deep sleep is essential for restoring the brain’s capacity to learn efficiently, especially in regions heavily activated during the day. However, causal evidence in humans has been lacking due to the inability to sleep deprive one target area while keeping the natural sleep pattern intact. Here we introduce a novel approach to focally perturb deep sleep in motor cortex, and investigate the consequences on behavioural and neurophysiological markers of neuroplasticity arising from dedicated motor practice. We show that the capacity to undergo neuroplastic changes is reduced by wakefulness but restored during unperturbed sleep. This restorative process is markedly attenuated when slow waves are selectively perturbed in motor cortex, demonstrating that deep sleep is a requirement for maintaining sustainable learning efficiency.

Deep sleep is hypothesized to restore the brain's capacity to learn. Here the authors provide causal evidence by specifically perturbing slow wave activity over the motor cortex during NREM sleep in humans and demonstrate a reduction in neurophysiological markers of plasticity and capacity for motor learning.