EEG recording and analysis for sleep research

Adult Inception of Ketogenic Diet Therapy Increases Sleep during the Dark Cycle in C57BL/6J Wild Type and Fragile X Mice

Sleep problems are a significant phenotype in children with fragile X syndrome. Our prior work assessed sleep-wake cycles in Fmr1KO male mice and wild type (WT) littermate controls in response to ketogenic diet therapy where mice were treated from weaning (postnatal day 18) through study completion (5-6 months of age). A potentially confounding issue with commencing treatment during an active period of growth is the significant reduction in weight gain in response to the ketogenic diet. The aim here was to employ sleep electroencephalography (EEG) to assess sleep-wake cycles in mice in response to the Fmr1 genotype and a ketogenic diet, with treatment starting at postnatal day 95. EEG results were compared with prior sleep outcomes to determine if the later intervention was efficacious, as well as with published rest-activity patterns to determine if actigraphy is a viable surrogate for sleep EEG. The data replicated findings that Fmr1KO mice exhibit sleep-wake patterns similar to wild type littermates during the dark cycle when maintained on a control purified-ingredient diet but revealed a genotype-specific difference during hours 4-6 of the light cycle of the increased wake (decreased sleep and NREM) state in Fmr1KO mice. Treatment with a high-fat, low-carbohydrate ketogenic diet increased the percentage of NREM sleep in both wild type and Fmr1KO mice during the dark cycle. Differences in sleep microstructure (length of wake bouts) supported the altered sleep states in response to ketogenic diet. Commencing ketogenic diet treatment in adulthood resulted in a 15% (WT) and 8.6% (Fmr1KO) decrease in body weight after 28 days of treatment, but not the severe reduction in body weight associated with starting treatment at weaning. We conclude that the lack of evidence for improved sleep during the light cycle (mouse sleep time) in Fmr1KO mice in response to ketogenic diet therapy in two studies suggests that ketogenic diet may not be beneficial in treating sleep problems associated with fragile X and that actigraphy is not a reliable surrogate for sleep EEG in mice.

Ultraconformal Skin-Interfaced Sensing Platform for Motion Artifact-Free Monitoring

Capable of directly capturing various physiological signals from human skin, skin-interfaced bioelectronics has emerged as a promising option for human health monitoring. However, the accuracy and reliability of the measured signals can be greatly affected by body movements or skin deformations (e.g., stretching, wrinkling, and compression). This study presents an ultraconformal, motion artifact-free, and multifunctional skin bioelectronic sensing platform fabricated by a simple and user-friendly laser patterning approach for sensing high-quality human physiological data. The highly conductive membrane based on the room-temperature coalesced Ag/Cu@Cu core-shell nanoparticles in a mixed solution of polymers can partially dissolve and locally deform in the presence of water to form conformal contact with the skin. The resulting sensors to capture improved electrophysiological signals upon various skin deformations and other biophysical signals provide an effective means to monitor health conditions and create human-machine interfaces. The highly conductive and stretchable membrane can also be used as interconnects to connect commercial off-the-shelf chips to allow extended functionalities, and the proof-of-concept demonstration is highlighted in an integrated pulse oximeter. The easy-to-remove feature of the resulting device with water further allows the device to be applied on delicate skin, such as the infant and elderly.

Sleep alterations as a function of 88 health indicators

BACKGROUND

Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health.

METHODS

We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications.

RESULTS

Of sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects.

CONCLUSIONS

Sleep alterations are present in various health conditions.

Sleep Architecture and Sleep EEG Alterations are Associated with Impaired Cognition Under Sleep Restriction

Purpose

Many studies have investigated the cognitive, emotional, and other impairments caused by sleep restriction. However, few studies have explored the relationship between cognitive performance and changes in sleep structure and electroencephalography (EEG) during sleep. The present study aimed to examine whether changes in sleep structure and EEG can account for cognitive impairment caused by sleep restriction.

Patients and Methods

Sixteen young adults spent five consecutive nights (adaptation 9h, baseline 8h, 1st restriction 6h, 2nd restriction 6h, and recovery 10h) in a sleep laboratory, with polysomnography recordings taken during sleep. Throughout waking periods in each condition, participants completed the psychomotor vigilance test (PVT), which measures vigilant attention, and the Go/No-Go task, which measures inhibition control.

Results

The results showed that sleep restriction significantly decreased the proportion of N1 and N2 sleep, increased the proportion of N3 sleep, and reduced the time spent awake after sleep onset (WASO) and sleep onset latency. Poorer performance on the PVT and Go/No Go task was associated with longer WASO, a larger proportion of N3 sleep, and a smaller proportion of N2 sleep. Additionally, the power spectral density of delta waves significantly increased after sleep restriction, and this increase predicted a decrease in vigilance and inhibition control the next day.

Conclusion

These findings suggest that sleep architecture and EEG signatures may partially explain cognitive impairment caused by sleep restriction.

Ketogenic Diet Affects Sleep Architecture in C57BL/6J Wild Type and Fragile X Mice

Nearly half of children with fragile X syndrome experience sleep problems including trouble falling asleep and frequent nighttime awakenings. The goals here were to assess sleep-wake cycles in mice in response to Fmr1 genotype and a dietary intervention that reduces hyperactivity. Electroencephalography (EEG) results were compared with published rest-activity patterns to determine if actigraphy is a viable surrogate for sleep EEG. Specifically, sleep-wake patterns in adult wild type and Fmr1KO littermate mice were recorded after EEG electrode implantation and the recordings manually scored for vigilance states. The data indicated that Fmr1KO mice exhibited sleep-wake patterns similar to wild type littermates when maintained on a control purified ingredient diet. Treatment with a high-fat, low-carbohydrate ketogenic diet increased the percentage of non-rapid eye movement (NREM) sleep in both wild type and Fmr1KO mice during the dark cycle, which corresponded to decreased activity levels. Treatment with a ketogenic diet flattened diurnal sleep periodicity in both wild type and Fmr1KO mice. Differences in several sleep microstructure outcomes (number and length of sleep and wake bouts) supported the altered sleep states in response to a ketogenic diet and were correlated with altered rest-activity cycles. While actigraphy may be a less expensive, reduced labor surrogate for sleep EEG during the dark cycle, daytime resting in mice did not correlate with EEG sleep states.

A new acquisition protocol for conducting studies with children: The science camp research experience

In the last 50 years, the study of brain development has brought major discoveries to education and medicine, changing the lives of millions of children and families. However, collecting behavioral and neurophysiological data from children has specific challenges, such as high rates of data loss and participant dropout. We have developed a science camp method to collect data from children using the benefits of positive peer interactions and interactive and engaging activities, to allow researchers to better collect data repeatedly and reliably from groups of children. A key advantage of this approach is that by increasing participant engagement, attention is also increased, thereby increasing data quality, reducing data loss, and lowering attrition rates. This protocol describes the step-by-step procedure for facilitation of a science camp, including behavioral, electrophysiological, and participatory engagement activities. As this method is robust but also flexible, we anticipate that it can also be applied to different demographics and research needs.

Objective sleep measures in chronic fatigue syndrome patients: A systematic review and meta-analysis

Patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) often report disrupted and unrefreshing sleep in association with worsened fatigue symptoms. However, the nature and magnitude of sleep architecture alteration in ME/CFS is not known, with studies using objective sleep measures in ME/CFS generating contradictory results. The current manuscript aimed to review and meta-analyse of case-control studies with objective sleep measures in ME/CSF. A search was conducted in PubMed, Scopus, Medline, Google Scholar, and Psychoinfo databases. After review, 24 studies were included in the meta-analysis, including 20 studies with 801 adults (ME/CFS = 426; controls = 375), and 4 studies with 477 adolescents (ME/CFS = 242; controls = 235), who underwent objective measurement of sleep. Adult ME/CFS patients spend longer time in bed, longer sleep onset latency, longer awake time after sleep onset, reduced sleep efficiency, decreased stage 2 sleep, more Stage 3, and longer rapid eye movement sleep latency. However, adolescent ME/CFS patients had longer time in bed, longer total sleep time, longer sleep onset latency, and reduced sleep efficiency. The meta-analysis results demonstrate that sleep is altered in ME/CFS, with changes seeming to differ between adolescent and adults, and suggesting sympathetic and parasympathetic nervous system alterations in ME/CFS.

Profile of sleep disturbances in patients with recurrent depressive disorder or bipolar affective disorder in a tertiary sleep disorders service

Bidirectional relationship between sleep disturbances and affective disorders is increasingly recognised, but its underlying mechanisms are far from clear, and there is a scarcity of studies that report on sleep disturbances in recurrent depressive disorder (RDD) and bipolar affective disorder (BPAD). To address this, we conducted a retrospective study of polysomnographic and clinical records of patients presenting to a tertiary sleep disorders clinic with affective disorders. Sixty-three BPAD patients (32 female; mean age ± S.D.: 41.8 ± 12.4 years) and 126 age- and gender-matched RDD patients (62 female; 41.5 ± 12.8) were studied. Whilst no significant differences were observed in sleep macrostructure parameters between BPAD and RDD patients, major differences were observed in comorbid sleep and physical disorders, both of which were higher in BPAD patients. Two most prevalent sleep disorders, namely obstructive sleep apnoea (OSA) (BPAD 50.8.0% vs RDD 29.3%, P = 0.006) and insomnia (BPAD 34.9% vs RDD 15.0%, P = 0.005) were found to be strongly linked with BPAD. In summary, in our tertiary sleep clinic cohort, no overt differences in the sleep macrostructure between BPAD and RDD patients were demonstrated. However, OSA and insomnia, two most prevalent sleep disorders, were found significantly more prevalent in patients with BPAD, by comparison to RDD patients. Also, BPAD patients presented with significantly more severe OSA, and with higher overall physical co-morbidity. Thus, our findings suggest an unmet/hidden need for earlier diagnosis of those with BPAD.

Testing the Accuracy of Wearable Technology to Assess Sleep Behaviour in Domestic Dogs: A Prospective Tool for Animal Welfare Assessment in Kennels

Sleep is a physiological process that has been shown to impact both physical and psychological heath of individuals when compromised; hence, it has the potential to be used as an indicator of animal welfare. Nonetheless, evaluating sleep in non-human species normally involves manipulation of the subjects (i.e., placement of electrodes on the cranium), and most studies are conducted in a laboratory setting, which limits the generalisability of information obtained, and the species investigated. In this study, we evaluated an alternative method of assessing sleep behaviour in domestic dogs, using a wearable sensor, and compared the measurements obtained to behavioural observations to evaluate accuracy. Differences between methods ranged from 0.13% to 59.3% for diurnal observations and 0.1% to 95.9% for nocturnal observations for point-by-point observations. Comparisons between methods showed significant differences in certain behaviours, such as inactivity and activity for diurnal recordings. However, total activity and total sleep recorded did not differ statistically between methods. Overall, the wearable technology tested was found to be a useful, and a less-time consuming, tool in comparison to direct behavioural observations for the evaluation of behaviours and their indication of wellbeing in dogs. The agreement between the wearable technology and directly observed data ranged from 75% to 99% for recorded behaviours, and these results are similar to previous findings in the literature.

Recent Progress in Long-Term Sleep Monitoring Technology

Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.

A Narrative Review on REM Sleep Deprivation: A Promising Non-Pharmaceutical Alternative for Treating Endogenous Depression

Endogenous depression represents a severe mental health condition projected to become one of the worldwide leading causes of years lived with disability. The currently available clinical and non-clinical interventions designed to alleviate endogenous depression-associated symptoms encounter a series of inconveniences, from the lack of intervention effectiveness and medication adherence to unpleasant side effects. In addition, depressive individuals tend to be more frequent users of primary care units, which markedly affects the overall treatment costs. In parallel with the growing incidence of endogenous depression, researchers in sleep science have discovered multiple links between rapid eye movement (REM) sleep patterns and endogenous depression. Recent findings suggest that prolonged periods of REM sleep are associated with different psychiatric disorders, including endogenous depression. In addition, a growing body of experimental work confidently describes REM sleep deprivation (REM-D) as the underlying mechanism of most pharmaceutical antidepressants, proving its utility as either an independent or adjuvant approach to alleviating the symptoms of endogenous depression. In this regard, REM-D is currently being explored for its potential value as a sleep intervention-based method for improving the clinical management of endogenous depression. Therefore, this narrative review represents a comprehensive inventory of the currently available evidence supporting the potential use of REM-D as a reliable, non-pharmaceutical approach for treating endogenous depression, or as an adjuvant practice that could improve the effectiveness of currently used medication.

John EK, Potesta CV, Gallagher MJ, Macdonald RL, Zhou C. Sleep slow-wave oscillations trigger seizures in a genetic epilepsy model of Dravet syndrome

Sleep is the preferential period when epileptic spike-wave discharges appear in human epileptic patients, including genetic epileptic seizures such as Dravet syndrome with multiple mutations including SCN1A mutation and GABAA receptor γ2 subunit Gabrg2Q390X mutation in patients, which presents more severe epileptic symptoms in female patients than male patients. However, the seizure onset mechanism during sleep still remains unknown. Our previous work has shown that the sleep-like state-dependent homeostatic synaptic potentiation can trigger epileptic spike-wave discharges in one transgenic heterozygous Gabrg2+/Q390X knock-in mouse model.1 Here, using this heterozygous knock-in mouse model, we hypothesized that slow-wave oscillations themselves in vivo could trigger epileptic seizures. We found that epileptic spike-wave discharges in heterozygous Gabrg2+/Q390X knock-in mice exhibited preferential incidence during non-rapid eye movement sleep period, accompanied by motor immobility/facial myoclonus/vibrissal twitching and more frequent spike-wave discharge incidence appeared in female heterozygous knock-in mice than male heterozygous knock-in mice. Optogenetically induced slow-wave oscillations in vivo significantly increased epileptic spike-wave discharge incidence in heterozygous Gabrg2+/Q390X knock-in mice with longer duration of non-rapid eye movement sleep or quiet-wakeful states. Furthermore, suppression of slow-wave oscillation-related homeostatic synaptic potentiation by 4-(diethylamino)-benzaldehyde injection (i.p.) greatly attenuated spike-wave discharge incidence in heterozygous knock-in mice, suggesting that slow-wave oscillations in vivo did trigger seizure activity in heterozygous knock-in mice. Meanwhile, sleep spindle generation in wild-type littermates and heterozygous Gabrg2+/Q390X knock-in mice involved the slow-wave oscillation-related homeostatic synaptic potentiation that also contributed to epileptic spike-wave discharge generation in heterozygous Gabrg2+/Q390X knock-in mice. In addition, EEG spectral power of delta frequency (0.1-4 Hz) during non-rapid eye movement sleep was significantly larger in female heterozygous Gabrg2+/Q390X knock-in mice than that in male heterozygous Gabrg2+/Q390X knock-in mice, which likely contributes to the gender difference in seizure incidence during non-rapid eye movement sleep/quiet-wake states of human patients. Overall, all these results indicate that slow-wave oscillations in vivo trigger the seizure onset in heterozygous Gabrg2+/Q390X knock-in mice, preferentially during non-rapid eye movement sleep period and likely generate the sex difference in seizure incidence between male and female heterozygous Gabrg2+/Q390X knock-in mice.

The Influence of Sleep Disturbance on Chronic Pain

PURPOSE OF REVIEW

The purpose of this review is to present an overview of common sleep disturbance pathologies and their impact on chronic pain, while examining various factors that are implicit in the relationship between sleep disturbance and chronic pain, including neurobiochemistry, anatomy, and systemic mediators, and reviewing recent and landmark literature.

RECENT FINDINGS

Earlier literature reviews and studies have introduced the bidirectional relationship between sleep disturbance and chronic pain; that is, impaired sleep may worsen chronic pain, and chronic pain causes sleep disturbance. However, more recent reviews and studies seem to show a more associative, rather than causative relationship. There have been recent studies that attempt to determine mechanisms that link sleep disturbance and chronic pain; the results of these studies were more varied, ultimately concluding that there may be a separate, yet-to-be discovered mechanism that shows the causative relationship between sleep disturbance and pain. There are several neurotransmitters that are involved in the mediation of chronic pain and sleep disturbance as separate entities, and some studies have shown that there may be mechanisms that govern both chronic pain and sleep disturbance as a single unit. Other neuroendocrine substances also serve to mediate chronic pain and sleep disturbance. All these substances are found to be associated with various sleep disorders and are also associated with chronic pain symptoms as well. Inflammation plays a role in chronic pain and sleep disturbance, with an increase in inflammatory substances and mediators associated with an increase or worsening in chronic pain symptoms and sleep disorders. The HPA axis plays a role in chronic pain and sleep disorders, influencing pain and sleep pathways through stress response, inflammation, and maintenance of homeostasis. There are several variables that influence both chronic pain and sleep disturbance, and more research into these variables may further our understanding into the complex pathways governing the influence of sleep disturbance on pain, and ultimately to improve treatment for this issue.

Restricted truncal sagittal movements of rapid eye movement behaviour disorder

Unlike sleep-walkers, patients with rapid-eye-movement-behaviour disorder (RBD) rarely leave the bed during the re-enactment of their dreams. RBD movements may be independent of spatial co-ordinates of the ‘outside-world’, and instead rely on (allocentric) brain-generated virtual space-maps, as evident by patients’ limited truncal/axial movements. To confirm this, a semiology analysis of video-polysomnography records of 38 RBD patients was undertaken and paradoxically restricted truncal/thoraco-lumbar movements during complex dream re-enactments demonstrated.

Basic characteristics of EEG epileptiform discharges triggered by lindane in a model of experimental prostatitis

Introduction: Chronic prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) is the most commonly diagnosed non-infectious prostatitis in urology. Studies have shown that CP/CPPS can induce neuroinflammation, which may result in CNS hyperexcitability and a tendency to develop epileptic seizures. Spike salvos are ictal EEG graph elements typical for the experimental model of lindane-induced seizures. There are a number of mathematical models for quantitative analysis of EEG, including the Fast Fourier Transform (FFT). It transforms the signal from time into the frequency domain, providing information on Power Spectral Densities (PSD). Aim: The aim of this study was to investigate the basic characteristics of epileptiform discharges induced by subconvulsive dose of lindane in rats, with experimentally induced CP/CPPS. Material and methods: CP/CPPS was induced by intraprostatic injection of 3% l-carrageenan in male Wistar albino rats. Animals with CP/CPPS were implanted with EEG registration electrodes, and then administered lindane (4 mg/kg, i.p, experimental group, n = 6 per group) or its solvent (DMSO, control group, n = 6 per group). An 8-channel EEG device was used in combination with software developed in the laboratory (NeuroSciLaBG). Ictal EEG epochs were extracted from the original signal and FFT analysis was performed to obtain information taking into account PSD in predefined frequency bands. Results: There was no ictal activity in the EEG of control animals. In experimental animals, ictal activity occurred and the mean duration of the ictal period was 2.06 s. FFT analysis revealed that the Alpha frequency range (7-15 Hz) was markedly dominant during ictal activity. Conclusion: The results of this study showed the characteristics of epileptiform discharges in animals with experimentally induced CP/CPPS. This study and animal model are suitable for future translational studies of the comorbidities of this syndrome.

Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study

PURPOSE

Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data.

METHODS

The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated.

RESULTS

The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively).

CONCLUSION

The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.

Dysfunction of the Glymphatic System as a Potential Mechanism of Perioperative Neurocognitive Disorders

Perioperative neurocognitive disorder (PND) frequently occurs in the elderly as a severe postoperative complication and is characterized by a decline in cognitive function that impairs memory, attention, and other cognitive domains. Currently, the exact pathogenic mechanism of PND is multifaceted and remains unclear. The glymphatic system is a newly discovered glial-dependent perivascular network that subserves a pseudo-lymphatic function in the brain. Recent studies have highlighted the significant role of the glymphatic system in the removal of harmful metabolites in the brain. Dysfunction of the glymphatic system can reduce metabolic waste removal, leading to neuroinflammation and neurological disorders. We speculate that there is a causal relationship between the glymphatic system and symptomatic progression in PND. This paper reviews the current literature on the glymphatic system and some perioperative factors to discuss the role of the glymphatic system in PND.

Prediction of Locomotor Activity by Infrared Motion Detector on Sleep-wake State in Mice

Objective

Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power.

Methods

The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b.

Results

In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p < 0.001).

Conclusion

This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.

Determination of the sleep-wake pattern and feasibility of NREM/REM discrimination using the non-invasive piezoelectric system in rats

The piezoelectric cage-floor sensors have been used to successfully dissect sleep patterns in mice based on signal features related to respiration and body movements. We studied performance of the piezoelectric system to quantify the sleep-wake pattern in the rat over 7 days of recording compared with a visual electroencephalogram/electromyogram scoring, and under two light/dark (LD12:12 and LD16:8) photoperiods leading to change in the 24-hr sleep characteristics (N = 7 per group). The total sleep time (%/24 hr) over the 7 days recording and hourly sleep time over the last 24-hr recording were not statistically different between methods under the two photoperiods. Both methods detected higher total sleep time with the LD16:8 photoperiod compared with LD12:12 (p < .05), and correlated significantly (p < .001) at light and dark periods during each photoperiod. The accuracies for discrimination of sleep-wake patterns between methods were 81.9% and 84.9% for LD12:12 and LD16:8, respectively. In addition, spectral analysis of the respiratory signal given by piezo during all 10-s periods of the corresponding non-rapid eye movement and rapid eye movement sleep periods recorded by electroencephalogram/electromyogram resulted in selection of 36 features that could be inserted in an automated non-rapid eye movement sleep and rapid eye movement sleep classification, with 90% accuracy with the electroencephalogram/electromyogram visual scoring. The piezo system proved to be a reliable non-invasive alternative to electroencephalogram recording to study total sleep time in rat, with feasibility to discriminate between non-rapid eye movement and rapid eye movement sleep stages. This will be interesting in pharmacological or bio-behavioural studies evaluating sleep patterns or the restorative functions of sleep in the body and the brain.

Cerebral metabolic rate of oxygen during transition from wakefulness to sleep measured with high temporal resolution OxFlow MRI with concurrent EEG

During slow-wave sleep, synaptic transmissions are reduced with a concomitant reduction in brain energy consumption. We used 3 Tesla MRI to noninvasively quantify changes in the cerebral metabolic rate of O2 (CMRO2) during wakefulness and sleep, leveraging the 'OxFlow' method, which provides venous O2 saturation (SvO2) along with cerebral blood flow (CBF). Twelve healthy subjects (31.3 ± 5.6 years, eight males) underwent 45-60 min of continuous scanning during wakefulness and sleep, yielding one image set every 3.4 s. Concurrent electroencephalography (EEG) data were available in eight subjects. Mean values of the metabolic parameters measured during wakefulness were stable, with coefficients of variation below 7% (average values: CMRO2 = 118 ± 12 µmol O2/min/100 g, SvO2 = 67.0 ± 3.7% HbO2, CBF = 50.6 ±4.3 ml/min/100 g). During sleep, on average, CMRO2 decreased 21% (range: 14%-32%; average nadir = 98 ± 16 µmol O2/min/100 g), while EEG slow-wave activity, expressed in terms of δ -power, increased commensurately. Following sleep onset, CMRO2 was found to correlate negatively with relative δ -power (r = -0.6 to -0.8, P < 0.005), and positively with heart rate (r = 0.5 to 0.8, P < 0.0005). The data demonstrate that OxFlow MRI can noninvasively measure dynamic changes in cerebral metabolism associated with sleep, which should open new opportunities to study sleep physiology in health and disease.

Bi-Temporal Anodal Transcranial Direct Current Stimulation during Slow-Wave Sleep Boosts Slow-Wave Density but Not Memory Consolidation

Slow-wave sleep (SWS) has been shown to promote long-term consolidation of episodic memories in hippocampo–neocortical networks. Previous research has aimed to modulate cortical sleep slow-waves and spindles to facilitate episodic memory consolidation. Here, we instead aimed to modulate hippocampal activity during slow-wave sleep using transcranial direct current stimulation in 18 healthy humans. A pair-associate episodic memory task was used to evaluate sleep-dependent memory consolidation with face–occupation stimuli. Pre- and post-nap retrieval was assessed as a measure of memory performance. Anodal stimulation with 2 mA was applied bilaterally over the lateral temporal cortex, motivated by its particularly extensive connections to the hippocampus. The participants slept in a magnetic resonance (MR)-simulator during the recordings to test the feasibility for a future MR-study. We used a sham-controlled, double-blind, counterbalanced randomized, within-subject crossover design. We show that stimulation vs. sham significantly increased slow-wave density and the temporal coupling of fast spindles and slow-waves. While retention of episodic memories across sleep was not affected across the entire sample of participants, it was impaired in participants with below-average pre-sleep memory performance. Hence, bi-temporal anodal direct current stimulation applied during sleep enhanced sleep parameters that are typically involved in memory consolidation, but it failed to improve memory consolidation and even tended to impair consolidation in poor learners. These findings suggest that artificially enhancing memory-related sleep parameters to improve memory consolidation can actually backfire in those participants who are in most need of memory improvement.

Evolutionary Origin of Distinct NREM and REM Sleep

Sleep is mandatory in most animals that have the nervous system and is universally observed in model organisms ranging from the nematodes, zebrafish, to mammals. However, it is unclear whether different sleep states fulfill common functions and are driven by shared mechanisms in these different animal species. Mammals and birds exhibit two obviously distinct states of sleep, i.e., non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep, but it is unknown why sleep should be so segregated. Studying sleep in other animal models might give us clues that help solve this puzzle. Recent studies suggest that REM sleep, or ancestral forms of REM sleep might be found in non-mammalian or -avian species such as reptiles. These observations suggest that REM sleep and NREM sleep evolved earlier than previously thought. In this review, we discuss the evolutionary origin of the distinct REM/NREM sleep states to gain insight into the mechanistic and functional reason for these two different types of sleep.

Patterns of Intrahemispheric EEG Asymmetry in Insomnia Sufferers: An Exploratory Study

Individuals with insomnia present unique patterns of electroencephalographic (EEG) asymmetry between homologous regions of each brain hemisphere, yet few studies have assessed asymmetry within the same hemisphere. Increase in intrahemispheric asymmetry during rapid eye movement (REM) sleep in good sleepers (GS) and disruption of REM sleep in insomnia sufferers (INS) both point out that this activity may be involved in the pathology of insomnia. The objective of the present exploratory study was to evaluate and quantify patterns of fronto-central, fronto-parietal, fronto-occipital, centro-parietal, centro-occipital and parieto-occipital intrahemispheric asymmetry in GS and INS, and to assess their association with sleep-wake misperception, daytime anxiety and depressive symptoms, as well as insomnia severity. This paper provides secondary analysis of standard EEG recorded in 43 INS and 19 GS for three nights in a sleep laboratory. Asymmetry measures were based on EEG power spectral analysis within 0.3–60 Hz computed between pairs of regions at frontal, central, parietal and occipital derivations. Repeated-measures ANOVAs were performed to assess group differences. Exploratory correlations were then performed on asymmetry and sleep-wake misperception, as well as self-reported daytime anxiety and depressive symptoms, and insomnia severity. INS presented increased delta and theta F3/P3 asymmetry during REM sleep compared with GS, positively associated with depressive and insomnia complaints. INS also exhibited decreased centro-occipital (C3/O1, C4/O2) and parieto-occipital (P3–O1, P4/O2) theta asymmetry during REM. These findings suggest that INS present specific patterns of intrahemispheric asymmetry, partially related to their clinical symptoms. Future studies may investigate the extent to which asymmetry is related to sleep-wake misperception or memory impairments.

Are Individuals with Low Trait Anxiety Better Suited to On-Call Work?

Research has indicated that individuals with certain traits may be better suited to shiftwork and non-standard working arrangements. However, no research has investigated how individual differences impact on-call outcomes. As such, this study investigated the impact of trait anxiety on sleep and performance outcomes on-call. Seventy male participants (20–35 years) completed an adaptation night, a control night, and two on-call nights in a laboratory. Trait anxiety was determined using the State Trait Anxiety Inventory (STAI) X-2, and participants completed the STAI X-1 prior to bed each night to assess state anxiety. Sleep was measured using polysomnography and quantitative electroencephalographic analysis. Performance was assessed using a 10-min psychomotor vigilance task (PVT) performed each day at 0930, 1200, 1430 and 1700 h. Data pooled from three separate but inter-related studies was used for these analyses. Results indicated that the effects of trait anxiety on state anxiety, sleep and performance outcomes on-call were generally limited. These findings suggest that on-call outcomes are not negatively affected by higher levels of trait anxiety.

Hyper-Activation of mPFC Underlies Specific Traumatic Stress-Induced Sleep-Wake EEG Disturbances

Sleep disturbances have been recognized as a core symptom of post-traumatic stress disorders (PTSD). However, the neural basis of PTSD-related sleep disturbances remains unclear. It has been challenging to establish the causality link between a specific brain region and traumatic stress-induced sleep abnormalities. Here, we found that single prolonged stress (SPS) could induce acute changes in sleep/wake duration as well as short- and long-term electroencephalogram (EEG) alterations in the isogenic mouse model. Moreover, the medial prefrontal cortex (mPFC) showed persistent high number of c-fos expressing neurons, of which more than 95% are excitatory neurons, during and immediately after SPS. Chemogenetic inhibition of the prelimbic region of mPFC during SPS could specifically reverse the SPS-induced acute suppression of delta power (1–4 Hz EEG) of non-rapid-eye-movement sleep (NREMS) as well as most of long-term EEG abnormalities. These findings suggest a causality link between hyper-activation of mPFC neurons and traumatic stress-induced specific sleep–wake EEG disturbances.

Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram

OBJECTIVE

We present a system for automated annotation of non-apnoea arousals using twelve signals from the polysomnogram (PSG) including airflow, six signals of electroencephalogram, the electrooculogram, chin electromyogram, oximetry signal, and chest and abdominal respiratory effort signals.

APPROACH

Fifty-nine time- and frequency-domain features were extracted from the twelve signals using 15 s epochs. Features from an epoch were combined with features from adjacent epochs and then processed with a bank of feed-forward networks that provided a probability estimate of the occurrence of a non-apnoea arousal event in every epoch. Data from the 2018 PhysioNet/Computing in Cardiology Challenge was used to develop and test the system. Ten-fold cross validation on the 994 PSGs of training data was used to compare the performance of different network configurations.

MAIN RESULTS

Our highest performing configuration utilised a bank of 30 feed-forward neural networks. Each network processed  ±4 epochs of features and each used a single hidden layer of 20 units. The performance of this configuration was evaluated on the independent test set of 989 PSGs and achieved an area under the receiver operator curve of 0.848 and an area under the precision-recall curve of 0.325 for correctly discriminating non-apnoea arousals from non-arousals samples.

SIGNIFICANCE

The classification performance results of our system demonstrate that automated annotation of non-apnoea arousals can be achieved with a high degree of reliability.

Quantifying interdependence using the missing joint ordinal patterns

In this paper, we develop the concept of forbidden/missing ordinal patterns into the forbidden/missing joint ordinal patterns and propose the ratio of the number of missing joint ordinal patterns (RMJPs) as a sign of interdependence. RMJP in a surrogate analysis can be used to differentiate the forbidden joint ordinal patterns from the missing joint ordinal patterns due to small sample effects. We first apply RMJP to the simulated time series: a two-component autoregressive fractionally integrated moving average process, the Hénon map, and the Rössler system using active control and discuss the effect of the length of the time series, embedding dimension, and noise contamination. RMJP has been proven to be capable of measuring the interdependence in the numerical simulation. Then, RMJP is further used on the electroencephalogram (EEG) time series for empirical analysis to explore the interdependence of brain waves. With results by RMJP obtained from a widely used open dataset of the sleep EEG time series from healthy subjects, we find that RMJP can be used to quantify the brain wave interdependence under different sleep/wake stages, reveal the overall sleep architecture, and indicate a higher level of interdependence as sleep gets deeper. The findings are consistent with existing knowledge in sleep medicine. The proposed RMJP method has shown its validity and applicability and may assist automatic sleep quantification or bring insight into the understanding of the brain activity during sleep. Furthermore, RMJP can be used on sleep EEG under various pathological conditions and in large-scale sleep studies, helping to investigate the mechanisms of the sleep process and neuron synchronization.

The Neurobiological Basis of Sleep and Sleep Disorders

The functions of sleep remain a mystery. Yet they must be important since sleep is highly conserved, and its chronic disruption is associated with various metabolic, psychiatric, and neurodegenerative disorders. This review will cover our evolving understanding of the mechanisms by which sleep is controlled and the complex relationship between sleep and disease states.

Does working memory improvement benefit from sleep in older adults?

Working Memory (WM), is an important factor influencing many higher-order cognitive functions that decline with age. Repetitive training appears to increase WM, yet the mechanisms underlying this improvement are not understood. Sleep has been shown to benefit long-term memory formation and may also play a role in WM enhancement in young adults. However, considering age-related decline in sleep, it is uninvestigated whether sleep will facilitate WM in older adults. In the present work, we investigated the impact of a nap, quiet wakefulness (QW) and active wakefulness (AW) on within-day training on the Operation Span (OSPAN) task in older adults. Improvement in WM was found following a nap and QW, but not active wake. Furthermore, better WM was associated with shared electrophysiological features, including slow oscillation (SO, 0.5-1 Hz) power in both the nap and QW, and greater coupling between SO and sigma (12-15 Hz) in the nap. In summary, our data suggest that WM improvement in older adults occurs opportunistically during offline periods that afford enhancement in slow oscillation power, and that further benefits may come with cross-frequency coupling of neural oscillations during sleep.

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI₁₋₃₀. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.

Wearable In-Ear Encephalography Sensor for Monitoring Sleep. Preliminary Observations from Nap Studies

RATIONALE

To date, EEG is the only quantifiable measure of the neural changes that define sleep. Although it is used widely for clinical testing, scalp-electrode EEG is costly and is poorly tolerated by sleeping patients.

OBJECTIVES

This was a pilot study to assess the agreement between EEG recordings obtained from a new ear-EEG sensor and those obtained simultaneously from standard scalp electrodes.

METHODS

Participants were four healthy men, 25 to 36 years of age. During naps, EEG tracings were recorded simultaneously from the ear sensor and from standard scalp electrodes. A clinical expert, blinded to the data collection, analyzed 30-second epochs of recordings from both devices, using standardized criteria. The agreement between scalp- and ear-recordings was assessed.

MEASUREMENTS AND MAIN RESULTS

We scored 360 epochs (scalp-EEG and ear-EEG), of which 254 (70.6%) were scored as non-REM sleep using scalp-EEG. The ear-EEG sensor had a sensitivity of 0.88 (95% confidence interval [CI], 0.82-0.92) and a specificity of 0.78 (95% CI, 0.70-0.84) in detecting N2/N3 sleep. The kappa coefficient between the scalp- and the ear-EEG was 0.65 (95% CI, 0.58-0.73). As a sleep monitor (all non-REM sleep stages vs. wake), the in-ear sensor had a sensitivity of 0.91 (95% CI, 0.87-0.94) and a specificity of 0.66 (95% CI, 0.56-0.75). The kappa coefficient was 0.60 (95% CI, 0.50-0.69).

CONCLUSIONS

Substantial agreement was observed between recordings derived from a new ear-EEG sensor and conventional scalp electrodes on four healthy volunteers during daytime naps.

Unraveling the Evolutionary Determinants of Sleep

Despite decades of intense study, the functions of sleep are still shrouded in mystery. The difficulty in understanding these functions can be at least partly attributed to the varied manifestations of sleep in different animals. Daily sleep duration can range from 4-20 hrs among mammals, and sleep can manifest throughout the brain, or it can alternate over time between cerebral hemispheres, depending on the species. Ecological factors are likely to have shaped these and other sleep behaviors during evolution by altering the properties of conserved arousal circuits in the brain. Nonetheless, core functions of sleep are likely to have arisen early and to have persisted to the present day in diverse organisms. This review will discuss the evolutionary forces that may be responsible for phylogenetic differences in sleep and the potential core functions that sleep fulfills.

Respiratory rate variability in sleeping adults without obstructive sleep apnea

Characterizing respiratory rate variability (RRV) in humans during sleep is challenging, since it requires the analysis of respiratory signals over a period of several hours. These signals are easily distorted by movement and volitional inputs. We applied the method of spectral analysis to the nasal pressure transducer signal in 38 adults with no obstructive sleep apnea, defined by an apnea‐hypopnea index <5, who underwent all‐night polysomnography (PSG). Our aim was to detect and quantitate RRV during the various sleep stages, including wakefulness. The nasal pressure transducer signal was acquired at 100 Hz and consecutive frequency spectra were generated for the length of the PSG with the Fast Fourier Transform. For each spectrum, we computed the amplitude ratio of the first harmonic peak to the zero frequency peak (H₁/DC), and defined as RRV as (100 − H₁/DC) %. RRV was greater during wakefulness compared to any sleep stage, including rapid‐eye‐movement. Furthermore, RRV correlated with the depth of sleep, being lowest during N3. Patients spent most their sleep time supine, but we found no correlation between RRV and body position. There was a correlation between respiratory rate and sleep stage, being greater in wakefulness than in any sleep stage. We conclude that RRV varies according to sleep stage. Moreover, spectral analysis of nasal pressure signal appears to provide a valid measure of RRV during sleep. It remains to be seen if the method can differentiate normal from pathological sleep patterns.

Automatic sleep stage classification using ear-EEG

Sleep assessment is of great importance in the diagnosis and treatment of sleep disorders. In clinical practice this is typically performed based on polysomnography recordings and manual sleep staging by experts. This procedure has the disadvantages that the measurements are cumbersome, may have a negative influence on the sleep, and the clinical assessment is labor intensive. Addressing the latter, there has recently been encouraging progress in the field of automatic sleep staging [1]. Furthermore, a minimally obtrusive method for recording EEG from electrodes in the ear (ear-EEG) has recently been proposed [2]. The objective of this study was to investigate the feasibility of automatic sleep stage classification based on ear-EEG. This paper presents a preliminary study based on recordings from a total of 18 subjects. Sleep scoring was performed by a clinical expert based on frontal, central and occipital region EEG, as well as EOG and EMG. 5 subjects were excluded from the study because of alpha wave contamination. In one subject the standard polysomnography was supplemented by ear-EEG. A single EEG channel sleep stage classifier was implemented using the same features and the same classifier as proposed in [1]. The performance of the single channel sleep classifier based on the scalp recordings showed an 85.7 % agreement with the manual expert scoring through 10-fold inter-subject cross validation, while the performance of the ear-EEG recordings was based on a 10-fold intra-subject cross validation and showed an 82 % agreement with the manual scoring. These results suggest that automatic sleep stage classification based on ear-EEG recordings may provide similar performance as compared to single channel scalp EEG sleep stage classification. Thereby ear-EEG may be a feasible technology for future minimal intrusive sleep stage classification.

Hybrid-Aware Model for Senior Wellness Service in Smart Home

Smart home technology with situation-awareness is important for seniors to improve safety and security. With the development of context-aware computing, wearable sensor technology, and ubiquitous computing, it is easier for seniors to manage their health problem in smart home environment. For monitoring senior activity in smart home, wearable, and motion sensors—such as respiration rate (RR), electrocardiography (ECG), body temperature, and blood pressure (BP)—were used for monitoring movements of seniors. For context-awareness, environmental sensors—such as gas, fire, smoke, dust, temperature, and light sensors—were used for senior location data collection. Based on senior activity, senior health status can be classified into positive and negative. Based on senior location and time, senior safety is classified into safe and emergency. In this paper, we propose a hybrid inspection service middleware for monitoring elderly health risk based on senior activity and location. This hybrid-aware model for the detection of abnormal status of seniors has four steps as follows: (1) data collection from biosensors and environmental sensors; (2) monitoring senior location and time of stay in each location using environmental sensors; (3) monitoring senior activity using biometric data; finally, (4) expectation-maximization based decision-making step recommending proper treatment based on a senior health risk ratio.

Neuroligin 3 R451C mutation alters electroencephalography spectral activity in an animal model of autism spectrum disorders

Human studies demonstrate that sleep impairment is a concurrent comorbidity of autism spectrum disorders (ASD), but its etiology remains largely uncertain. One of the prominent theories of ASD suggests that an imbalance in synaptic excitation/inhibition may contribute to various aspects of ASD, including sleep impairments. Following the identification of Nlgn3^R451C mutation in patients with ASD, its effects on synaptic transmission and social behaviours have been examined extensively in the mouse model. However, the contributory role of this mutation to sleep impairments in ASD remains unknown. In this study, we showed that Nlgn3^R451C knock-in mice, an established genetic model for ASD, exhibited normal duration and distribution of sleep/wake states but significantly altered electroencephalography (EEG) power spectral profiles for wake and sleep.

Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG.

Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks

An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value <; 0.05).

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10–20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10–16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of “thermodynamic” equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium.

Ultrasound-Guided Second Trimester Fetal Electroencephalography in Two Pregnant Volunteers: A Technical Note

BACKGROUND AND PURPOSE

Data from electroencephalography (EEG) in preterm infants and neonates suggest that electroencephalographic abnormalities are likely present during antepartum period or fetal stage. We describe our initial effort to record fetal EEG with emphasis on technical aspects since such technique has not been developed as a viable and reproducible method.

METHODS

The fetal heads were located in two pregnant volunteers (29 years old and 26 years old) with 28 weeks of gestation by ultrasound imaging. Four surface electrodes were placed on maternal abdominal surface approximately 5 cm anterior to the lateral aspect of fetal head as confirmed by acoustic shadowing of the electrodes on ultrasound. A second set of recordings were performed in first volunteer with four electrodes placed on the maternal abdominal surface in front of the uterus distant to fetal head.

RESULTS

The analysis in the first volunteer demonstrated that the activity consisted of mixture of theta (4-7 Hz) and delta (1-4 Hz) waves which formed approximately 70% and 30% of the background rhythm. The analysis in the second volunteer demonstrated that the activity consisted of mixture of theta and delta, which formed approximately 30% and 70% of the background rhythm. There was superimposed beta (13-30 Hz) and infrequently gamma (30-100 Hz) activity. Maternal electrocardiographic QRS complex and respiratory artifacts were seen in some leads. During second recording, the electrodes distant to fetal head recorded predominantly delta with lesser proportion of theta waves.

CONCLUSIONS

We report our initial experience in acquisition of fetal EEG. The findings reported here will help to guide further research into developing methodologies for the performance of fetal EEG.

Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions

Within the framework of 'Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

Wheel running improves REM sleep and attenuates stress-induced flattening of diurnal rhythms in F344 rats

Regular physical activity produces resistance to the negative health consequences of stressor exposure. One way that exercise may confer stress resistance is by reducing the impact of stress on diurnal rhythms and sleep; disruptions of which contribute to stress-related disease including mood disorders. Given the link between diurnal rhythm disruptions and stress-related disorders and that exercise both promotes stress resistance and is a powerful non-photic biological entrainment cue, we tested if wheel running could reduce stress-induced disruptions of sleep/wake behavior and diurnal rhythms. Adult, male F344 rats with or without access to running wheels were instrumented for biotelemetric recording of diurnal rhythms of locomotor activity, heart rate, core body temperature (CBT), and sleep (i.e. REM, NREM, and WAKE) in the presence of a 12 h light/dark cycle. Following 6 weeks of sedentary or exercise conditions, rats were exposed to an acute stressor known to disrupt diurnal rhythms and produce behaviors associated with mood disorders. Prior to stressor exposure, exercise rats had higher CBT, more locomotor activity during the dark cycle, and greater %REM during the light cycle relative to sedentary rats. NREM and REM sleep were consolidated immediately following peak running to a greater extent in exercise, compared to sedentary rats. In response to stressor exposure, exercise rats expressed higher stress-induced hyperthermia than sedentary rats. Stressor exposure disrupted diurnal rhythms in sedentary rats; and wheel running reduced these effects. Improvements in sleep and reduced diurnal rhythm disruptions following stress could contribute to the health promoting and stress protective effects of exercise.