Frontal beta-theta network during REM sleep

Consciousness and sleep

Sleep is a universal, essential biological process. It is also an invaluable window on consciousness. It tells us that consciousness can be lost but also that it can be regained, in all its richness, when we are disconnected from the environment and unable to reflect. By considering the neurophysiological differences between dreaming and dreamless sleep, we can learn about the substrate of consciousness and understand why it vanishes. We also learn that the ongoing state of the substrate of consciousness determines the way each experience feels regardless of how it is triggered-endogenously or exogenously. Dreaming consciousness is also a window on sleep and its functions. Dreams tell us that the sleeping brain is remarkably lively, recombining intrinsic activation patterns from a vast repertoire, freed from the requirements of ongoing behavior and cognitive control.

An EEG Signature of MCH Neuron Activities Predicts Cocaine Seeking

BACKGROUND

Identifying biomarkers that predict substance use disorder (SUD) propensity may better strategize anti-addiction treatment. The melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH) critically mediates interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers.

METHODS

Surface EEG signals and real-time Ca2+ activities of LH MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors.

RESULTS

An EEG waveform derivative - a modified theta-to-delta ratio (EEG Ratio) - accurately tracks real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through REMS manipulations, wakefulness, circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEG Ratio suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEG Ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEG Ratio also exhibited advantages over conventional REMS measures for the predictions.

CONCLUSIONS

The identified EEG Ratio may serve as a non-invasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker predicting drug use propensity.

Mystery of the memory engram: History, current knowledge, and unanswered questions

The quest to understand the memory engram has intrigued humans for centuries. Recent technological advances, including genetic labelling, imaging, optogenetic and chemogenetic techniques, have propelled the field of memory research forward. These tools have enabled researchers to create and erase memory components. While these innovative techniques have yielded invaluable insights, they often focus on specific elements of the memory trace. Genetic labelling may rely on a particular immediate early gene as a marker of activity, optogenetics may activate or inhibit one specific type of neuron, and imaging may capture activity snapshots in a given brain region at specific times. Yet, memories are multifaceted, involving diverse arrays of neuronal subpopulations, circuits, and regions that work in concert to create, store, and retrieve information. Consideration of contributions of both excitatory and inhibitory neurons, micro and macro circuits across brain regions, the dynamic nature of active ensembles, and representational drift is crucial for a comprehensive understanding of the complex nature of memory.

An EEG Signature of MCH Neuron Activities Predicts Cocaine Seeking

Background

Identifying biomarkers that predict substance use disorder (SUD) propensity may better strategize anti-addiction treatment. The melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH) critically mediates interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers.

Methods

Surface EEG signals and real-time Ca2+ activities of LH MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors.

Results

An EEG waveform derivative - a modified theta-to-delta ratio (EEG Ratio) - accurately tracks real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through REMS manipulations, wakefulness, circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEG Ratio suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEG Ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEG Ratio also exhibited advantages over conventional REMS measures for the predictions.

Conclusions

The identified EEG Ratio may serve as a non-invasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker predicting drug use propensity.

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.

Post-training sleep modulates motor adaptation and task-related beta oscillations

Motor adaptation reflects the ability of the brain's sensorimotor system to flexibly deal with environmental changes to generate effective motor behaviour. Whether sleep contributes to the consolidation of motor adaptation remains controversial. In this study, we investigated the impact of sleep on motor adaptation and its neurophysiological correlates in a novel motor adaptation task that leverages a highly automatised motor skill, that is, typing. We hypothesised that sleep-associated memory consolidation would benefit motor adaptation and induce modulations in task-related beta band (13-30 Hz) activity during adaptation. Healthy young male experts in typing on the regular computer keyboard were trained to type on a vertically mirrored keyboard while brain activity was recorded using electroencephalography. Typing performance was assessed either after a full night of sleep with polysomnography or a similar period of daytime wakefulness. Results showed improved motor adaptation performance after nocturnal sleep but not after daytime wakefulness, and decreased beta power: (a) during mirrored typing as compared with regular typing; and (b) in the post-sleep versus the pre-sleep mirrored typing sessions. Furthermore, the slope of the electroencephalography signal, a measure of aperiodic brain activity, decreased during mirrored as compared with regular typing. Changes in the electroencephalography spectral slope from pre- to post-sleep mirrored typing sessions were correlated with changes in task performance. Finally, increased fast sleep spindle density (13-15 Hz) during the night following motor adaptation training was predictive of successful motor adaptation. These findings suggest that post-training sleep modulates neural activity supporting adaptive motor functions.

Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data

A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.

EEG spectral power during REM sleep in patients with frontal brain tumor

BACKGROUND

The main objective of this research was to analyze the characteristics of electrical activity in the brain during REM (Rapid Eye Movements) sleep, by using an experimental model a pathology that affects the frontal lobes, such as brain tumors. In addition to determining the impact of variables such as the frontal area (dorsolateral, medial and orbital), laterality and size of the lesion; as well as the demographic and clinical characteristics of the patients evaluated.

METHODS

By using polysomnographic recordings, 10 patients were evaluated. We obtained power spectra through a homemade program. For quantitative EEG (Electroencephalogram) (qEEG) analysis, the Fast Fourier Transform (FFT) algorithm was used to obtain the spectral power of each participant, channel, and frequency band.

RESULTS

Sleep architecture and spectral power was found to be modified in patients compared to normative values. Other sociodemographic and clinical characteristics of the patients were also influenced, such as age range and antiepileptic drugs.

CONCLUSIONS

Brain tumors in the frontal lobe can modify the rhythmogenesis of REM sleep, possibly due to changes of brain plasticity as an effect of the pathology. In addition to this, through this study we were able to show the association between neuroanatomical and functional changes, on the characteristics of brain electrical activity in patients with frontal brain tumor. Finally, this qEEG analysis technique allows, on the one hand, to deepen the knowledge and relationship between psychophysiological processes and, on the other hand, to be able to guide therapeutic decisions.

Emotional memories are enhanced when reactivated in slow wave sleep, but impaired when reactivated in REM

Sleep supports memory consolidation. However, it is not completely clear how different sleep stages contribute to this process. While rapid eye movement sleep (REM) has been traditionally implicated in the processing of emotionally charged material, recent studies indicate a role for slow wave sleep (SWS) in strengthening the memories of emotional stimuli. Here, to directly examine which sleep stage is primarily involved in emotional memory consolidation, we used targeted memory reactivation (TMR) in REM and SWS during a daytime nap. We also examined neural oscillations associated with TMR-related changes in memory. Contrary to our hypothesis, reactivation of emotional stimuli during REM led to impaired memory. Meanwhile, reactivation of emotional stimuli in SWS improved memory and was strongly correlated with the product of times spent in REM and SWS (%SWS Ã- %REM). When this variable was taken into account, reactivation significantly enhanced memory, with larger reactivation benefits compared to reactivation in REM. Notably, sleep spindle activity was modulated by emotional valence, and delta/theta activity was correlated with the memory benefit for both emotional and neutral items. Finally, we found no evidence that emotional memories benefited from TMR more than did neutral ones. Our results provide direct evidence for a complementary role of both REM and SWS in emotional memory consolidation, and suggest that REM may separately facilitate forgetting. In addition, our findings expand upon recent evidence indicating a link between sleep spindles and emotional processing.

Emotional Memory Processing during REM Sleep with Implications for Post-Traumatic Stress Disorder

REM sleep is important for the processing of emotional memories, including fear memories. Rhythmic interactions, especially in the theta band, between the medial prefrontal cortex (mPFC) and limbic structures are thought to play an important role, but the ways in which memory processing occurs at a mechanistic and circuits level are largely unknown. To investigate how rhythmic interactions lead to fear extinction during REM sleep, we used a biophysically based model that included the infralimbic cortex (IL), a part of the mPFC with a critical role in suppressing fear memories. Theta frequency (4-12 Hz) inputs to a given cell assembly in IL, representing an emotional memory, resulted in the strengthening of connections from the IL to the amygdala and the weakening of connections from the amygdala to the IL, resulting in the suppression of the activity of fear expression cells for the associated memory. Lower frequency (4 Hz) theta inputs effected these changes over a wider range of input strengths. In contrast, inputs at other frequencies were ineffective at causing these synaptic changes and did not suppress fear memories. Under post-traumatic stress disorder (PTSD) REM sleep conditions, rhythmic activity dissipated, and 4 Hz theta inputs to IL were ineffective, but higher-frequency (10 Hz) theta inputs to IL induced changes similar to those seen with 4 Hz inputs under normal REM sleep conditions, resulting in the suppression of fear expression cells. These results suggest why PTSD patients may repeatedly experience the same emotionally charged dreams and suggest potential neuromodulatory therapies for the amelioration of PTSD symptoms.SIGNIFICANCE STATEMENT Rhythmic interactions in the theta band between the mPFC and limbic structures are thought to play an important role in processing emotional memories, including fear memories, during REM sleep. The infralimbic cortex (IL) in the mPFC is thought to play a critical role in suppressing fear memories. We show that theta inputs to the IL, unlike other frequency inputs, are effective in producing synaptic changes that suppress the activity of fear expression cells associated with a given memory. Under PTSD REM sleep conditions, lower-frequency (4 Hz) theta inputs to the IL do not suppress the activity of fear expression cells associated with the given memory but, surprisingly, 10 Hz inputs do. These results suggest potential neuromodulatory therapies for PTSD.

Cortico-cortical and thalamo-cortical connectivity during non-REM and REM sleep: Insights from intracranial recordings in humans

OBJECTIVE

To study changes of thalamo-cortical and cortico-cortical connectivity during wakefulness, non-Rapid Eye Movement (non-REM) sleep, including N2 and N3 stages, and REM sleep, using stereoelectroencephalography (SEEG) recording in humans.

METHODS

We studied SEEG recordings of ten patients during wakefulness, non-REM sleep and REM sleep, in seven brain regions of interest including the thalamus. We calculated directed and undirected functional connectivity using a measure of non-linear correlation coefficient h².

RESULTS

The thalamus was more connected to other brain regions during N2 stage and REM sleep than during N3 stage during which cortex was more connected than the thalamus. We found two significant directed links: the first from the prefrontal region to the lateral parietal region in the delta band during N3 sleep and the second from the thalamus to the insula during REM sleep.

CONCLUSIONS

These results showed that cortico-cortical connectivity is more prominent in N3 stage than in N2 and REM sleep. During REM sleep we found significant thalamo-insular connectivity, with a driving role of the thalamus.

SIGNIFICANCE

We found a pattern of cortical connectivity during N3 sleep concordant with antero-posterior traveling slow waves. The thalamus seemed particularly involved as a hub of connectivity during REM sleep.

A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data

In recent years, the use of convolutional neural networks (CNNs) for raw resting-state electroencephalography (EEG) analysis has grown increasingly common. However, relative to earlier machine learning and deep learning methods with manually extracted features, CNNs for raw EEG analysis present unique problems for explainability. As such, a growing group of methods have been developed that provide insight into the spectral features learned by CNNs. However, spectral power is not the only important form of information within EEG, and the capacity to understand the roles of specific multispectral waveforms identified by CNNs could be very helpful. In this study, we present a novel model visualization-based approach that adapts the traditional CNN architecture to increase interpretability and combines that inherent interpretability with a systematic evaluation of the model via a series of novel explainability methods. Our approach evaluates the importance of spectrally distinct first-layer clusters of filters before examining the contributions of identified waveforms and spectra to cluster importance. We evaluate our approach within the context of automated sleep stage classification and find that, for the most part, our explainability results are highly consistent with clinical guidelines. Our approach is the first to systematically evaluate both waveform and spectral feature importance in CNNs trained on resting-state EEG data.

Ensemble of coupling forms and networks among brain rhythms as function of states and cognition

The current paradigm in brain research focuses on individual brain rhythms, their spatiotemporal organization, and specific pairwise interactions in association with physiological states, cognitive functions, and pathological conditions. Here we propose a conceptually different approach to understanding physiologic function as emerging behavior from communications among distinct brain rhythms. We hypothesize that all brain rhythms coordinate as a network to generate states and facilitate functions. We analyze healthy subjects during rest, exercise, and cognitive tasks and show that synchronous modulation in the micro-architecture of brain rhythms mediates their cross-communications. We discover that brain rhythms interact through an ensemble of coupling forms, universally observed across cortical areas, uniquely defining each physiological state. We demonstrate that a dynamic network regulates the collective behavior of brain rhythms and that network topology and links strength hierarchically reorganize with transitions across states, indicating that brain-rhythm interactions play an essential role in generating physiological states and cognition.

Default mode network and neural phase synchronization in healthy aging: A resting state EEG study

Aging is associated with altered brain connectivity within the default mode network (DMN). Although research using functional magnetic resonance imaging has quantified age-related alterations in functional connectivity within this network during resting state, it is less clear how this may be reflected in electrophysiological measures, and how this relates to cognitive performance in older adults. The aim of this study was to quantify age differences in phase synchrony of the DMN during resting state, with particular focus on connectivity between the anterior node (i.e., medial prefrontal cortex, or mPFC) and other associated regions in this network. Electroencephalography was recorded from 55 younger adults (18-30 years, 28 females) and 34 older adults (64-88 years, 16 females) in two resting state conditions (eyes-open and -closed). Source-level functional connectivity was quantified using phase-locking value (PLV) with a spatial filter of six sources of interest, and were subjected to data-driven permutation testing between groups from 1 to 50 Hz. Older adults also completed tests of memory, language, executive functioning, and processing speed. Findings indicated decreased connectivity in the alpha2 range for older than younger adults between the mPFC and other DMN regions including the left angular gyrus and bilateral lateral temporal cortices, the latter of which were associated with lower performance in semantic fluency and executive functioning in older adults. Furthermore, greater PLV in theta and beta bands between the mPFC and posterior cingulate regions was found in older than younger adults. These results suggest age-related changes in DMN functional connectivity are non-uniform and frequency-dependent, and may reflect poorer performance in cognitive domains thought to decline with aging.

Pediatric Sleep Apnea: The Overnight Electroencephalogram as a Phenotypic Biomarker

Pediatric obstructive sleep apnea (OSA) is a prevalent disorder that disrupts sleep and is associated with neurocognitive and behavioral negative consequences, potentially hampering the development of children for years. However, its relationships with sleep electroencephalogram (EEG) have been scarcely investigated. Here, our main objective was to characterize the overnight EEG of OSA-affected children and its putative relationships with polysomnographic measures and cognitive functions. A two-step analysis involving 294 children (176 controls, 57% males, age range: 5-9 years) was conducted for this purpose. First, the activity and irregularity of overnight EEG spectrum were characterized in the typical frequency bands by means of relative spectral power and spectral entropy, respectively: δ1 (0.1-2 Hz), δ2 (2-4 Hz), θ (4-8 Hz), α (8-13 Hz), σ (10-16 Hz), β1 (13-19 Hz), β2 (19-30 Hz), and γ (30-70 Hz). Then, a correlation network analysis was conducted to evaluate relationships between them, six polysomnography variables (apnea-hypopnea index, respiratory arousal index, spontaneous arousal index, overnight minimum blood oxygen saturation, wake time after sleep onset, and sleep efficiency), and six cognitive scores (differential ability scales, Peabody picture vocabulary test, expressive vocabulary test, design copying, phonological processing, and tower test). We found that as the severity of the disease increases, OSA broadly affects sleep EEG to the point that the information from the different frequency bands becomes more similar, regardless of activity or irregularity. EEG activity and irregularity information from the most severely affected children were significantly associated with polysomnographic variables, which were coherent with both micro and macro sleep disruptions. We hypothesize that the EEG changes caused by OSA could be related to the occurrence of respiratory-related arousals, as well as thalamic inhibition in the slow oscillation generation due to increases in arousal levels aimed at recovery from respiratory events. Furthermore, relationships between sleep EEG and cognitive scores emerged regarding language, visual-spatial processing, and executive function with pronounced associations found with EEG irregularity in δ1 (Peabody picture vocabulary test and expressive vocabulary test maximum absolute correlations 0.61 and 0.54) and β2 (phonological processing, 0.74; design copying, 0.65; and Tow 0.52). Our results show that overnight EEG informs both sleep alterations and cognitive effects of pediatric OSA. Moreover, EEG irregularity provides new information that complements and expands the classic EEG activity analysis. These findings lay the foundation for the use of sleep EEG to assess cognitive changes in pediatric OSA.

Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.

Phase-locked auditory stimulation of theta oscillations during rapid eye movement sleep

Auditory closed-loop stimulation is a non-invasive technique that has been widely used to augment slow oscillations during non-rapid eye movement sleep. Based on the principles of closed-loop stimulation, we developed a novel protocol for manipulating theta activity (3-7 Hz) in rapid eye movement (REM) sleep. Sixteen healthy young adults were studied in two overnight conditions: Stimulation and Sham. In the Stimulation condition, 1 s of 5 Hz amplitude-modulated white noise was delivered upon detection of two supra-threshold theta cycles throughout REM sleep. In the Sham condition, corresponding time points were marked but no stimulation was delivered. Auditory stimulation entrained EEG activity to 5 Hz and evoked a brief (~0.5 s) increase in theta power. Interestingly, this initial theta surge was immediately followed by a prolonged (~3 s) period of theta suppression. Stimulation also induced a prolonged (~2 s) increase in beta power. Our results provide the first demonstration that the REM sleep theta rhythm can be manipulated in a targeted manner via auditory stimulation. Accordingly, auditory stimulation might offer a fruitful avenue for investigating REM sleep electrophysiology and its relationship to behavior.

Sharp Wave-Ripples in Human Amygdala and Their Coordination with Hippocampus during NREM Sleep

Cooperative interactions between the amygdala and hippocampus are widely regarded as critical for overnight emotional processing of waking experiences, but direct support from the human brain for such a dialog is absent. Using overnight intracranial recordings in 4 presurgical epilepsy patients (3 female), we discovered ripples within human amygdala during nonrapid eye movement (NREM) sleep, a brain state known to contribute to affective processing. Like hippocampal ripples, amygdala ripples are associated with sharp waves, linked to sleep spindles, and tend to co-occur with their hippocampal counterparts. Moreover, sharp waves and ripples are temporally linked across the 2 brain structures, with amygdala ripples occurring during hippocampal sharp waves and vice versa. Combined with further evidence of interregional sharp-wave and spindle synchronization, these findings offer a potential physiological substrate for the NREM-sleep-dependent consolidation and regulation of emotional experiences.

Analyzing human sleep EEG: A methodological primer with code implementation

Recent years have witnessed a surge in human sleep electroencephalography (EEG) studies, employing increasingly sophisticated analysis strategies to relate electrophysiological activity to cognition and disease. However, properly calculating and interpreting metrics used in contemporary sleep EEG requires attention to numerous theoretical and practical signal-processing details that are not always obvious. Moreover, the vast number of outcome measures that can be derived from a single dataset inflates the risk of false positives and threatens replicability. We review several methodological issues related to 1) spectral analysis, 2) montage choice, 3) extraction of phase and amplitude information, 4) surrogate construction, and 5) minimizing false positives, illustrating both the impact of methodological choices on downstream results, and the importance of checking processing steps through visualization and simplified examples. By presenting these issues in non-mathematical form, with sleep-specific examples, and with code implementation, this paper aims to instill a deeper appreciation of methodological considerations in novice and non-technical audiences, and thereby help improve the quality of future sleep EEG studies.

Dynamic network interactions among distinct brain rhythms as a hallmark of physiologic state and function

Brain rhythms are associated with a range of physiologic states, and thus, studies have traditionally focused on neuronal origin, temporal dynamics and fundamental role of individual brain rhythms, and more recently on specific pair-wise interactions. Here, we aim to understand integrated physiologic function as an emergent phenomenon of dynamic network interactions among brain rhythms. We hypothesize that brain rhythms continuously coordinate their activations to facilitate physiologic states and functions. We analyze healthy subjects during sleep, and we demonstrate the presence of stable interaction patterns among brain rhythms. Probing transient modulations in brain wave activation, we discover three classes of interaction patterns that form an ensemble representative for each sleep stage, indicating an association of each state with a specific network of brain-rhythm communications. The observations are universal across subjects and identify networks of brain-rhythm interactions as a hallmark of physiologic state and function, providing new insights on neurophysiological regulation with broad clinical implications.

Phase-based coordination of hippocampal and neocortical oscillations during human sleep

During sleep, new memories undergo a gradual transfer from hippocampal (HPC) to neocortical (NC) sites. Precisely timed neural oscillations are thought to mediate this sleep-dependent memory consolidation, but exactly how sleep oscillations instantiate the HPC-NC dialog remains elusive. Employing overnight invasive electroencephalography in ten neurosurgical patients, we identified three broad classes of phase-based communication between HPC and lateral temporal NC. First, we observed interregional phase synchrony for non-rapid eye movement (NREM) spindles, and N2 and rapid eye movement (REM) theta activity. Second, we found asymmetrical N3 cross-frequency phase-amplitude coupling between HPC slow oscillations (SOs) and NC activity spanning the delta to high-gamma/ripple bands, but not in the opposite direction. Lastly, N2 theta and NREM spindle synchrony were themselves modulated by HPC SOs. These forms of interregional communication emphasize the role of HPC SOs in the HPC-NC dialog, and may offer a physiological basis for the sleep-dependent reorganization of mnemonic content.

The generation and propagation of the human alpha rhythm

The alpha rhythm is the longest-studied brain oscillation and has been theorized to play a key role in cognition. Still, its physiology is poorly understood. In this study, we used microelectrodes and macroelectrodes in surgical epilepsy patients to measure the intracortical and thalamic generators of the alpha rhythm during quiet wakefulness. We first found that alpha in both visual and somatosensory cortex propagates from higher-order to lower-order areas. In posterior cortex, alpha propagates from higher-order anterosuperior areas toward the occipital pole, whereas alpha in somatosensory cortex propagates from associative regions toward primary cortex. Several analyses suggest that this cortical alpha leads pulvinar alpha, complicating prevailing theories of a thalamic pacemaker. Finally, alpha is dominated by currents and firing in supragranular cortical layers. Together, these results suggest that the alpha rhythm likely reflects short-range supragranular feedback, which propagates from higher- to lower-order cortex and cortex to thalamus. These physiological insights suggest how alpha could mediate feedback throughout the thalamocortical system.

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

Objective: To explore whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). Methods: Thirty-eight drug-naive patients with PI, and 44 healthy controls (HC) underwent resting-state functional MR imaging. Voxel-wise functional connectivity strength (FCS), large-scale functional connectivity (large-scale FC) and regional homogeneity (ReHo) were calculated for each participant. We used support vector machine (SVM) with the three types of metrics as features separately to classify patients from healthy controls. Then we evaluated its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS) in the patients' group. Results: The best classifier could reach up to an accuracy of 81.5%, with a sensitivity of 84.9%, specificity of 79.1%, and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right anterior insular cortex (BA48), left precuneus (BA7), and left middle frontal gyrus (BA8) showed high classification weights. In addition, the right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) were the overlapping regions between MVPA and group comparison. Correlation analysis showed that FCS in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS, respectively. Conclusion: The current study suggests abnormal FCS in right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) might serve as a potential neuromarkers for PI.

Heterogeneous profiles of coupled sleep oscillations in human hippocampus

Cross-frequency coupling of sleep oscillations is thought to mediate memory consolidation. While the hippocampus is deemed central to this process, detailed knowledge of which oscillatory rhythms interact in the sleeping human hippocampus is lacking. Combining intracranial hippocampal and non-invasive electroencephalography from twelve neurosurgical patients, we characterized spectral power and coupling during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Hippocampal coupling was extensive, with the majority of channels expressing spectral interactions. NREM consistently showed delta–ripple coupling, but ripples were also modulated by slow oscillations (SOs) and sleep spindles. SO–delta and SO–theta coupling, as well as interactions between delta/theta and spindle/beta frequencies also occurred. During REM, limited interactions between delta/theta and beta frequencies emerged. Moreover, oscillatory organization differed substantially between i) hippocampus and scalp, ii) sites along the anterior-posterior hippocampal axis, and iii) individuals. Overall, these results extend and refine our understanding of hippocampal sleep oscillations.

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Sleep oscillations in human hippocampus exhibit cross-frequency coupling during non-rapid eye movement sleep

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Coupling occurs between various frequency pairs, including slow oscillation, delta, theta, spindle, beta, and ripple bands

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Oscillatory organization varies between hippocampus and scalp, sites along the hippocampal axis, and individuals

Dream experiences and the neural correlates of perceptual consciousness and cognitive access

This paper approaches the debate whether perceptual consciousness requires cognitive access from the perspective of dream studies, and investigates what kind of findings could support the opposing views of this debate. Two kinds of arguments are discussed, one that claims that the hypoactivity of the dorsolateral prefrontal cortex in rapid eye movement sleep is directly relevant, and another that proposes that locating the neural correlates of dream experiences can indirectly inform the debate. It is argued that under closer reflection, neither the classical claim about dorsolateral prefrontal cortex hypoactivity nor the more recent emphasis on general posterior hot zone activity during dreaming stand up to scrutiny. White dreaming is identified as the phenomenon that, nevertheless, holds the most promise to have an impact on the debate. Going beyond the topic if studying dreams can contribute to this debate, it is argued that cognitive access is not a monolithic phenomenon, and its neural correlates are not well understood. There seems to be a relevant form of cognitive access that can operate in the absence of activity in the dorsolateral prefrontal cortex, and maybe also in the whole frontal region. If so, then exclusive posterior activation during conscious experiences might very well be compatible with the hypothesis that perceptual consciousness requires cognitive access.This article is part of the theme issue 'Perceptual consciousness and cognitive access'.

EEG Frontal Alpha Asymmetry and Dream Affect: Alpha Oscillations over the Right Frontal Cortex during REM Sleep and Presleep Wakefulness Predict Anger in REM Sleep Dreams

Affective experiences are central not only to our waking life but also to rapid eye movement (REM) sleep dreams. Despite our increasing understanding of the neural correlates of dreaming, we know little about the neural correlates of dream affect. Frontal alpha asymmetry (FAA) is considered a marker of affective states and traits as well as affect regulation in the waking state. Here, we explored whether FAA during REM sleep and during evening resting wakefulness is related to affective experiences in REM sleep dreams. EEG recordings were obtained from 17 human participants (7 men) who spent 2 nights in the sleep laboratory. Participants were awakened 5 min after the onset of every REM stage after which they provided a dream report and rated their dream affect. Two-minute preawakening EEG segments were analyzed. Additionally, 8 min of evening presleep and morning postsleep EEG were recorded during resting wakefulness. Mean spectral power in the alpha band (8-13 Hz) and corresponding FAA were calculated over the frontal (F4-F3) sites. Results showed that FAA during REM sleep, and during evening resting wakefulness, predicted ratings of dream anger. This suggests that individuals with greater alpha power in the right frontal hemisphere may be less able to regulate (i.e., inhibit) strong affective states, such as anger, in dreams. Additionally, FAA was positively correlated across wakefulness and REM sleep. Together, these findings imply that FAA may serve as a neural correlate of affect regulation not only in the waking but also in the dreaming state.SIGNIFICANCE STATEMENT We experience emotions not only during wakefulness but also during dreaming. Despite our increasing understanding of the neural correlates of dreaming, we know little about the neural correlates of dream emotions. Here we used electroencephalography to explore how frontal alpha asymmetry (FAA)-the relative difference in alpha power between the right and left frontal cortical areas that is associated with emotional processing and emotion regulation in wakefulness-is related to dream emotions. We show that individuals with greater FAA (i.e., greater right-sided alpha power) during rapid eye movement sleep, and during evening wakefulness, experience more anger in dreams. FAA may thus reflect the ability to regulate emotions not only in the waking but also in the dreaming state.

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.

Sequential Neural Activity in Primary Motor Cortex during Sleep

Sequential firing of neurons during sleep is thought to play a role in the consolidation of learning. However, direct evidence for such sequence replay is limited to only a few brain areas and sleep states mainly in rodents. Using a custom-designed wearable neural data logger and chronically implanted electrodes, we made long-term recordings of neural activity in the primary motor cortex of two female nonhuman primates during free behavior and natural sleep. We used the local field potential (LFP) spectrogram to characterize sleep cycles, and examined firing rates, correlations, and sequential firing of neurons at different frequency bands through the cycle. Slow-wave sleep (SWS) was characterized by low neural firing rates and high synchrony, reflecting slow oscillations between cortical down and up states. However, the order in which neurons entered up states was similar to the sequence of neural activity observed at low frequencies during waking behavior. In addition, we found evidence of brief bursts of theta oscillation, associated with non-SWS states, during which neurons fired in strikingly regular sequential order phase-locked to the LFP. Theta sequences were preserved between waking and sleep, but appeared not to resemble the order of neural activity observed at lower frequencies. The sequential firing of neurons during slow oscillations and theta bursts may contribute to the consolidation of procedural memories during sleep.

SIGNIFICANCE STATEMENT Replay of sequential neural activity during sleep is believed to support consolidation of daytime learning. Despite a wealth of studies investigating sequential replay in association with episodic and spatial memory, it is unknown whether similar sequences occur in motor areas during sleep. Within long-term neural recordings from monkey motor cortex, we found two distinct patterns of sequential activity during different phases of the natural sleep cycle. Slow-wave sleep was associated with delta-band sequences that resembled low-frequency activity during movement, while occasional brief bursts of theta oscillation were associated with a different order of sequential firing. Our results are the first report of sequential sleep replay in the motor cortex, which may play an important role in consolidation of procedural learning.

Electrical activity of the human amygdala during all-night sleep and wakefulness

OBJECTIVE

The aim of the present work was to characterize the dynamics of the human amygdala across the different sleep stages and during wakefulness.

METHODS

Simultaneous intracranial electrical recordings of the amygdala, hippocampus, and scalp electroencephalography during spontaneous sleep polysomnography in four patients suffering from epilepsy were analyzed.

RESULTS

Power spectrum of the amygdala revealed no differences between rapid eye movement (REM) and wakefulness for all frequencies except higher power at 9 Hz during wakefulness and some low Gamma frequencies. Conversely, higher power was observed in non-REM (NREM) sleep than wakefulness for Delta, Theta and Sigma.

CONCLUSIONS

Our results showed similar activity in the amygdala between wakefulness and REM sleep suggesting that the amygdala is as active in REM as during wakefulness. The higher power in Sigma frequencies during NREM sleep suggests that amygdala slow activity may play a significant role during NREM in concurrence with hippocampal activity.

SIGNIFICANCE

While studies have described the metabolic activity of the human amygdala during sleep, our results show the corresponding electrical pattern during the whole night, pointing out an increase of slow activity during NREM sleep that might be subjected to similar influences as other subcortical brain structures, such as the hippocampus.

Active Sleep Promotes Functional Connectivity in Developing Sensorimotor Networks

A ubiquitous feature of active (REM) sleep in mammals and birds is its relative abundance in early development. In rat pups across the first two postnatal weeks, active sleep promotes the expression of synchronized oscillatory activity within and between cortical and subcortical sensorimotor structures. Sensory feedback from self-generated myoclonic twitches - which are produced exclusively during active sleep - also triggers neural oscillations in those structures. We have proposed that one of the functions of active sleep in early infancy is to provide a context for synchronizing developing structures. Specifically, neural oscillations contribute to a variety of neurodevelopmental processes, including synapse formation, neuronal differentiation and migration, apoptosis, and the refinement of topographic maps. In addition, synchronized oscillations promote functional connectivity between distant brain areas. Consequently, any condition or manipulation that restricts active sleep can, in turn, deprive the infant animal of substantial sensory experience, resulting in atypical developmental trajectories.

The sleep EEG spectrum is a sexually dimorphic marker of general intelligence

The shape of the EEG spectrum in sleep relies on genetic and anatomical factors and forms an individual "EEG fingerprint". Spectral components of EEG were shown to be connected to mental ability both in sleep and wakefulness. EEG sleep spindle correlates of intelligence, however, exhibit a sexual dimorphism, with a more pronounced association to intelligence in females than males. In a sample of 151 healthy individuals, we investigated how intelligence is related to spectral components of full-night sleep EEG, while controlling for the effects of age. A positive linear association between intelligence and REM anterior beta power was found in females but not males. Transient, spindle-like "REM beta tufts" are described in the EEG of healthy subjects, which may reflect the functioning of a recently described cingular-prefrontal emotion and motor regulation network. REM sleep frontal high delta power was a negative correlate of intelligence. NREM alpha and sigma spectral power correlations with intelligence did not unequivocally remain significant after multiple comparisons correction, but exhibited a similar sexual dimorphism. These results suggest that the neural oscillatory correlates of intelligence in sleep are sexually dimorphic, and they are not restricted to either sleep spindles or NREM sleep.

Sleep-Dependent Oscillatory Synchronization: A Role in Fear Memory Consolidation

Sleep plays an important role in memory consolidation through the facilitation of neuronal plasticity; however, how sleep accomplishes this remains to be completely understood. It has previously been demonstrated that neural oscillations are an intrinsic mechanism by which the brain precisely controls neural ensembles. Inter-regional synchronization of these oscillations is also known to facilitate long-range communication and long-term potentiation (LTP). In the present study, we investigated how the characteristic rhythms found in local field potentials (LFPs) during non-REM and REM sleep play a role in emotional memory consolidation. Chronically implanted bipolar electrodes in the lateral amygdala (LA), dorsal and ventral hippocampus (DH, VH), and the infra-limbic (IL), and pre-limbic (PL) prefrontal cortex were used to record LFPs across sleep-wake activity following each day of a Pavlovian cued fear conditioning paradigm. This resulted in three principle findings: (1) theta rhythms during REM sleep are highly synchronized between regions; (2) the extent of inter-regional synchronization during REM and non-REM sleep is altered by FC and EX; (3) the mean phase difference of synchronization between the LA and VH during REM sleep predicts changes in freezing after cued fear extinction. These results both oppose a currently proposed model of sleep-dependent memory consolidation and provide a novel finding which suggests that the role of REM sleep theta rhythms in memory consolidation may rely more on the relative phase-shift between neural oscillations, rather than the extent of phase synchronization.

Lack of Responsiveness during the Onset and Offset of Sevoflurane Anesthesia Is Associated with Decreased Awake-Alpha Oscillation Power

Anesthetic drugs are typically administered to induce altered states of arousal that range from sedation to general anesthesia (GA). Systems neuroscience studies are currently being used to investigate the neural circuit mechanisms of anesthesia-induced altered arousal states. These studies suggest that by disrupting the oscillatory dynamics that are associated with arousal states, anesthesia-induced oscillations are a putative mechanism through which anesthetic drugs produce altered states of arousal. However, an empirical clinical observation is that even at relatively stable anesthetic doses, patients are sometimes intermittently responsive to verbal commands during states of light sedation. During these periods, prominent anesthesia-induced neural oscillations such as slow-delta (0.1–4 Hz) oscillations are notably absent. Neural correlates of intermittent responsiveness during light sedation have been insufficiently investigated. A principled understanding of the neural correlates of intermittent responsiveness may fundamentally advance our understanding of neural dynamics that are essential for maintaining arousal states, and how they are disrupted by anesthetics. Therefore, we performed a high-density (128 channels) electroencephalogram (EEG) study (n = 8) of sevoflurane-induced altered arousal in healthy volunteers. We administered temporally precise behavioral stimuli every 5 s to assess responsiveness. Here, we show that decreased eyes-closed, awake-alpha (8–12 Hz) oscillation power is associated with lack of responsiveness during sevoflurane effect-onset and -offset. We also show that anteriorization—the transition from occipitally dominant awake-alpha oscillations to frontally dominant anesthesia induced-alpha oscillations—is not a binary phenomenon. Rather, we suggest that periods, which were defined by lack of responsiveness, represent an intermediate brain state. We conclude that awake-alpha oscillation, previously thought to be an idling rhythm, is associated with responsiveness to behavioral stimuli.