Daytime dexmedetomidine sedation with closed-loop acoustic stimulation alters slow wave sleep homeostasis in healthy adults

Background

The alpha-2 adrenergic agonist dexmedetomidine induces EEG patterns resembling those of non-rapid eye movement (NREM) sleep. Fulfilment of slow wave sleep (SWS) homeostatic needs would address the assumption that dexmedetomidine induces functional biomimetic sleep states.

Methods

In-home sleep EEG recordings were obtained from 13 healthy participants before and after dexmedetomidine sedation. Dexmedetomidine target-controlled infusions and closed-loop acoustic stimulation were implemented to induce and enhance EEG slow waves, respectively. EEG recordings during sedation and sleep were staged using modified American Academy of Sleep Medicine criteria. Slow wave activity (EEG power from 0.5 to 4 Hz) was computed for NREM stage 2 (N2) and NREM stage 3 (N3/SWS) epochs, with the aggregate partitioned into quintiles by time. The first slow wave activity quintile served as a surrogate for slow wave pressure, and the difference between the first and fifth quintiles as a measure of slow wave pressure dissipation.

Results

Compared with pre-sedation sleep, post-sedation sleep showed reduced N3 duration (mean difference of -17.1 min, 95% confidence interval -30.0 to -8.2, P=0.015). Dissipation of slow wave pressure was reduced (P=0.02). Changes in combined durations of N2 and N3 between pre- and post-sedation sleep correlated with total dexmedetomidine dose, (r=-0.61, P=0.03).

Conclusions

Daytime dexmedetomidine sedation and closed-loop acoustic stimulation targeting EEG slow waves reduced N3/SWS duration and measures of slow wave pressure dissipation on the post-sedation night in healthy young adults. Thus, the paired intervention induces sleep-like states that fulfil certain homeostatic NREM sleep needs in healthy young adults.

Clinical trial registration

ClinicalTrials.gov NCT04206059.

Response to Hypoglossal Nerve Stimulation Changes With Body Mass Index and Supine Sleep

Importance

Hypoglossal nerve stimulation (HGNS) is a potential alternative therapy for obstructive sleep apnea (OSA), but its efficacy in a clinical setting and the impact of body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) on treatment response remain unclear.

Objective

To investigate whether HGNS therapy is effective for patients with OSA, whether HGNS can treat supine OSA, and whether there are associations between BMI and treatment response.

Design, Setting, and Participants

In this cohort study, adult patients with OSA implanted with HGNS at the Washington University Medical Center in St Louis from April 2019 to January 2023 were included. Data were analyzed from January 2023 to January 2024.

Exposure

HGNS.

Main Outcomes and Measures

Multivariable logistic regression was performed to assess associations between HGNS treatment response and both BMI and supine sleep. Treatment response was defined as 50% reduction or greater in preimplantation Apnea-Hypopnea Index (AHI) score and postimplantation AHI of less than 15 events per hour.

Results

Of 76 included patients, 57 (75%) were male, and the median (IQR) age was 61 (51-68) years. A total of 59 patients (78%) achieved a treatment response. There was a clinically meaningful reduction in median (IQR) AHI, from 29.3 (23.1-42.8) events per hour preimplantation to 5.3 (2.6-12.3) events per hour postimplantation (Hodges-Lehman difference of 23.0; 95% CI, 22.6-23.4). In adjusted analyses, patients with BMI of 32 to 35 had 75% lower odds of responding to HGNS compared with those with a BMI of 32 or less (odds ratio, 0.25; 95% CI, 0.07-0.94). Of 44 patients who slept in a supine position, 17 (39%) achieved a treatment response, with a clinically meaningful reduction in median (IQR) supine AHI from 46.3 (33.6-63.2) events per hour preimplantation to 21.8 (4.30-42.6) events per hour postimplantation (Hodges-Lehman difference of 24.6; 95% CI, 23.1-26.5). In adjusted analysis, BMI was associated with lower odds of responding to HGNS with supine AHI treatment response (odds ratio, 0.39; 95% CI, 0.04-2.59), but the imprecision of the estimate prevents making a definitive conclusion.

Conclusions and Relevance

This study adds to the growing body of literature supporting the use of HGNS for OSA treatment. Sleep medicine clinicians should consider informing patients that higher BMI and supine sleeping position may decrease therapeutic response to HGNS. Future research is needed to replicate these findings in larger, more diverse cohorts, which would facilitate the optimization of treatment strategies and patient counseling for HGNS therapy.

Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data

BACKGROUND

Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired.

NEW METHOD

A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep.

RESULTS

Sleep states were classified with an accuracy of 84% and Cohen's kappa of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner.

COMPARISON WITH EXISTING METHOD

On a 3-hour WFCI recording, the CNN-BiLSTM achieved a kappa of 0.67, comparable to a kappa of 0.65 corresponding to the human EEG/EMG-based scoring.

CONCLUSIONS

The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep.

Validation of Deep Learning-based Sleep State Classification

Deep learning methods have been developed to classify sleep states of mouse electroencephalogram (EEG) and electromyogram (EMG) recordings with accuracy reported as high as 97%. However, when applied to independent datasets, with a variety of experimental and recording conditions, sleep state classification accuracy often drops due to distributional shift. Mixture z-scoring, a pre-processing standardization of EEG/EMG signals, has been suggested to account for these variations. This study sought to validate mixture z-scoring in combination with a deep learning method on an independent dataset. The open-source software Accusleep, which implements mixture z-scoring in combination with deep learning via a convolutional neural network, was used to classify sleep states in 12, three-hour EEG/EMG recordings from mice sleeping in a head-fixed position. Mixture z-scoring with deep learning classified sleep states on two independent recordings with 85-92% accuracy and a Cohen's κ of 0.66-0.71. These results validate mixture z-scoring in combination with deep learning to classify sleep states with the potential for widespread use.

Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity

Normal aging is associated with a variety of neurologic changes including declines in cognition, memory, and motor activity. These declines correlate with neuronal changes in synaptic structure and function. Degradation of brain network activity and connectivity represents a likely mediator of age-related functional deterioration resulting from these neuronal changes. Human studies have demonstrated both general decreases in spontaneous cortical activity and disruption of cortical networks with aging. Current techniques used to study cerebral network activity are hampered either by limited spatial resolution (e.g. electroencephalography, EEG) or limited temporal resolution (e.g., functional magnetic resonance imaging, fMRI). Here we utilize mesoscale imaging of neuronal activity in Thy1-GCaMP6f mice to characterize neuronal network changes in aging with high spatial resolution across a wide frequency range. We show that while evoked activity is unchanged with aging, spontaneous neuronal activity decreases across a wide frequency range (0.01–4 Hz) involving all regions of the cortex. In contrast to this global reduction in cortical power, we found that aging is associated with functional connectivity (FC) deterioration of select networks including somatomotor, cingulate, and retrosplenial nodes. These changes are corroborated by reductions in homotopic FC and node degree within somatomotor and visual cortices. Finally, we found that whole-cortex delta power and delta band node degree correlate with exploratory activity in young but not aged animals. Together these data suggest that aging is associated with global declines in spontaneous cortical activity and focal deterioration of network connectivity, and that these reductions may be associated with age-related behavioral declines.

A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice

Temporal correlation analysis of spontaneous brain activity (e.g., Pearson "functional connectivity," FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

BACKGROUND

Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed.

NEW METHOD

A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM.

RESULTS

Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered.

COMPARISON WITH EXISTING METHOD

On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring.

CONCLUSIONS

The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.

Sleep and longitudinal cognitive performance in preclinical and early symptomatic Alzheimer's disease

Sleep monitoring may provide markers for future Alzheimer's disease; however, the relationship between sleep and cognitive function in preclinical and early symptomatic Alzheimer's disease is not well understood. Multiple studies have associated short and long sleep times with future cognitive impairment. Since sleep and the risk of Alzheimer's disease change with age, a greater understanding of how the relationship between sleep and cognition changes over time is needed. In this study, we hypothesized that longitudinal changes in cognitive function will have a non-linear relationship with total sleep time, time spent in non-REM and REM sleep, sleep efficiency and non-REM slow wave activity. To test this hypothesis, we monitored sleep-wake activity over 4-6 nights in 100 participants who underwent standardized cognitive testing longitudinally, APOE genotyping, and measurement of Alzheimer's disease biomarkers, total tau and amyloid-β42 in the CSF. To assess cognitive function, individuals completed a neuropsychological testing battery at each clinical visit that included the Free and Cued Selective Reminding test, the Logical Memory Delayed Recall assessment, the Digit Symbol Substitution test and the Mini-Mental State Examination. Performance on each of these four tests was Z-scored within the cohort and averaged to calculate a preclinical Alzheimer cognitive composite score. We estimated the effect of cross-sectional sleep parameters on longitudinal cognitive performance using generalized additive mixed effects models. Generalized additive models allow for non-parametric and non-linear model fitting and are simply generalized linear mixed effects models; however, the linear predictors are not constant values but rather a sum of spline fits. We found that longitudinal changes in cognitive function measured by the cognitive composite decreased at low and high values of total sleep time (P < 0.001), time in non-REM (P < 0.001) and REM sleep (P < 0.001), sleep efficiency (P < 0.01) and <1 Hz and 1-4.5 Hz non-REM slow wave activity (P < 0.001) even after adjusting for age, CSF total tau/amyloid-β42 ratio, APOE ε4 carrier status, years of education and sex. Cognitive function was stable over time within a middle range of total sleep time, time in non-REM and REM sleep and <1 Hz slow wave activity, suggesting that certain levels of sleep are important for maintaining cognitive function. Although longitudinal and interventional studies are needed, diagnosing and treating sleep disturbances to optimize sleep time and slow wave activity may have a stabilizing effect on cognition in preclinical or early symptomatic Alzheimer's disease.

Closed-Loop Acoustic Stimulation During Sedation with Dexmedetomidine (CLASS-D): Protocol for a Within-Subject, Crossover, Controlled, Interventional Trial with Healthy Volunteers

INTRODUCTION

The relative power of slow-delta oscillations in the electroencephalogram (EEG), termed slow-wave activity (SWA), correlates with level of unconsciousness. Acoustic enhancement of SWA has been reported for sleep states, but it remains unknown if pharmacologically induced SWA can be enhanced using sound. Dexmedetomidine is a sedative whose EEG oscillations resemble those of natural sleep. This pilot study was designed to investigate whether SWA can be enhanced using closed-loop acoustic stimulation during sedation (CLASS) with dexmedetomidine.

METHODS

Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine (CLASS-D) is a within-subject, crossover, controlled, interventional trial with healthy volunteers. Each participant will be sedated with a dexmedetomidine target-controlled infusion (TCI). Participants will undergo three CLASS conditions in a multiple crossover design: in-phase (phase-locked to slow-wave upslopes), anti-phase (phase-locked to slow-wave downslopes) and sham (silence). High-density EEG recordings will assess the effects of CLASS across the scalp. A volitional behavioral task and sequential thermal arousals will assess the anesthetic effects of CLASS. Ambulatory sleep studies will be performed on nights immediately preceding and following the sedation session. EEG effects of CLASS will be assessed using linear mixed-effects models. The impacts of CLASS on behavior and arousal thresholds will be assessed using logistic regression modeling. Parametric modeling will determine differences in sleepiness and measures of sleep homeostasis before and after sedation.

RESULTS

The primary outcome of this pilot study is the effect of CLASS on EEG slow waves. Secondary outcomes include the effects of CLASS on the following: performance of a volitional task, arousal thresholds, and subsequent sleep.

DISCUSSION

This investigation will elucidate 1) the potential of exogenous sensory stimulation to potentiate SWA during sedation; 2) the physiologic significance of this intervention; and 3) the connection between EEG slow-waves observed during sleep and sedation.

ANA Webinars: implementation of a conference-based virtual networking event

OBJECTIVE

To describe the design and implementation of a virtual network event at the American Neurological Association (ANA) annual meeting led by the Junior and Early Career Member (JECM) Committee.

METHODS

We designed a one-hour virtual networking session featuring three 15-minute small group meetings preceded and followed by general remarks. Each small group session consisted of one senior mentor, a junior/early career faculty moderator, and three to four junior/early career mentees. All participants completed an exit survey to evaluate perceived benefit of this event.

RESULTS

We recruited 103 mentees, 26 moderators, and 26 mentors for the event. Mentees were primarily at the resident training level or above (17% students). 56% of registered mentees, 100% of moderators and 96% of mentors attended the event for a total of 110 participants. Due to mentee attrition, each room contained 2-3 mentees. 90% of respondents felt the session met their goals very well or extremely well. Further, 99% felt this session was at least comparable to in-person networking at conferences and 60% felt this session was better than in-person networking.

INTERPRETATION

Virtual networking sessions between junior and senior academic neurologists are feasible and are at least comparable to, if not better than, in-person conference networking. Future events should consider nuanced mechanisms of matching mentors and mentees, inclusion of ad hoc small groups to foster organic networking, and measures to safeguard against mentee attrition. Future studies should evaluate the long-term benefits of this event to determine if virtual networking should be utilized moving forward.

0126 Local Slow Wave Sleep and Post-Stroke Brain Repair

Introduction

Recent evidence suggests that slow wave sleep (SWS) is important for synaptic plasticity and brain repair following stroke. Previous studies described a progressive increase in whole cortex and contralesional regional delta power during sleep after stroke, suggesting a global increase in SWS. However, these studies did not distinguish between the effects of global vs. local SWS. We hypothesized that local changes in SWS delta power would parallel changes in the functional remapping and circuit repair.

Methods

To study SWS in living mice we used Thy-1-GCaMP6f mice (n=12), serially imaged (baseline, 24 hours, weeks 1, 4,) during sleep following photothrombotic stroke of the left forepaw somatosensory cortex (S1FP). An optical fluorescence imaging system (OFI) was used to image whole-cortex neuronal activity. The evolution of local delta activity was compared across three ROIs: 1) infarct, 2) perilesional remapped, and 3) perilesional non-remapped left.

Results

The photothrombotic infarct encompassed the left S1FP stimulus map, resulting in significant attenuation of S1FP evoked responses at week 1; however, a small region of activation was retained in posterior left S1FP (peri-lesional remapped). The infarct region demonstrated a decrease in delta power during sleep; however, the perilesional region of future remapping exhibited a rebound in focal delta power at 1 week after an initial decline at 24 hours. In the perilesional non-remapped area delta power decreased, but did not increase until week 4. We also observed an early wide-spread increase in delta power at 24 hours and week 1 that decreased on week 4.

Conclusion

With the high spatial resolution of OFI, we find that SWS is disrupted throughout the brain following focal ischemia. These data suggest that local SWS selectively increases in the region of remapping prior to repair of that circuit and that local SWS may play a role in brain repair following stroke.

Support

AASM Foundation #183-PA-18, #201-BS-18

Reduced non-rapid eye movement sleep is associated with tau pathology in early Alzheimer's disease

In Alzheimer's disease (AD), deposition of insoluble amyloid-β (Aβ) is followed by intracellular aggregation of tau in the neocortex and subsequent neuronal cell loss, synaptic loss, brain atrophy, and cognitive impairment. By the time even the earliest clinical symptoms are detectable, Aβ accumulation is close to reaching its peak and neocortical tau pathology is frequently already present. The period in which AD pathology is accumulating in the absence of cognitive symptoms represents a clinically relevant time window for therapeutic intervention. Sleep is increasingly recognized as a potential marker for AD pathology and future risk of cognitive impairment. Previous studies in animal models and humans have associated decreased non-rapid eye movement (NREM) sleep slow wave activity (SWA) with Aβ deposition. In this study, we analyzed cognitive performance, brain imaging, and cerebrospinal fluid (CSF) AD biomarkers in participants enrolled in longitudinal studies of aging. In addition, we monitored their sleep using a single-channel electroencephalography (EEG) device worn on the forehead. After adjusting for multiple covariates such as age and sex, we found that NREM SWA showed an inverse relationship with AD pathology, particularly tauopathy, and that this association was most evident at the lowest frequencies of NREM SWA. Given that our study participants were predominantly cognitively normal, this suggested that changes in NREM SWA, especially at 1 to 2 Hz, might be able to discriminate tau pathology and cognitive impairment either before or at the earliest stages of symptomatic AD.

Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia

Modulation of brain state, e.g., by anesthesia, alters the correlation structure of spontaneous activity, especially in the delta band. This effect has largely been attributed to the ∼ 1    Hz slow oscillation that is characteristic of anesthesia and nonrapid eye movement (NREM) sleep. However, the effect of the slow oscillation on correlation structures and the spectral content of spontaneous activity across brain states (including NREM) has not been comprehensively examined. Further, discrepancies between activity dynamics observed with hemoglobin versus calcium (GCaMP6) imaging have not been reconciled. Lastly, whether the slow oscillation replaces functional connectivity (FC) patterns typical of the alert state, or superimposes on them, remains unclear. Here, we use wide-field calcium imaging to study spontaneous cortical activity in awake, anesthetized, and naturally sleeping mice. We find modest brain state-dependent changes in infraslow correlations but larger changes in GCaMP6 delta correlations. Principal component analysis of GCaMP6 sleep/anesthesia data in the delta band revealed that the slow oscillation is largely confined to the first three components. Removal of these components revealed a correlation structure strikingly similar to that observed during wake. These results indicate that, during NREM sleep/anesthesia, the slow oscillation superimposes onto a canonical FC architecture.

Ziprasidone as a Potential Abortive Therapy for Status Migrainosus

Migraine headache is among the most prevalent neurologic disorders. Status migrainosus often leads to hospitalization, and multiple medications are sometimes required for symptomatic relief. In 2008, neurologists at our institution started using the atypical antipsychotic ziprasidone as an abortive medication for status migrainosus. The Clinical Investigation Data Exploration Repository was used to search for patients admitted to the Barnes-Jewish Hospital inpatient neurology service with diagnoses of "headache" or "migraine." Patients were identified as having status migrainosus if they met the International Headache Society criteria for a migraine lasting >72 hours. Clinical records of identified patients were then entered into a secure online database (REDCap). Between 2008 and 2015, a total of 34 patients received 10 to 40 mg of ziprasidone for the treatment of status migrainosus. Among patients who received ziprasidone, headache severity decreased 5.68 ± 3.0 points on a 10-point scale, from admission to discharge. Ziprasidone was the last abortive medication added prior to discharge in 65% of cases. The 30-day readmission rate for migraine headache in patients who received ziprasidone was 12%. Ziprasidone was well tolerated, with side effects limited to a mild dystonic reaction (n = 1), rhinorrhea (n = 1), and a prolonged QTc of 495 milliseconds (n = 1). This observational study suggests that ziprasidone may be a safe, effective abortive medication for the treatment of status migrainosus. Further studies comparing ziprasidone to standard of care are warranted.

Comparison of a single-channel EEG sleep study to polysomnography

An accurate home sleep study to assess electroencephalography (EEG)-based sleep stages and EEG power would be advantageous for both clinical and research purposes, such as for longitudinal studies measuring changes in sleep stages over time. The purpose of this study was to compare sleep scoring of a single-channel EEG recorded simultaneously on the forehead against attended polysomnography. Participants were recruited from both a clinical sleep centre and a longitudinal research study investigating cognitively normal ageing and Alzheimer's disease. Analysis for overall epoch-by-epoch agreement found strong and substantial agreement between the single-channel EEG compared to polysomnography (κ = 0.67). Slow wave activity in the frontal regions was also similar when comparing the single-channel EEG device to polysomnography. As expected, Stage N1 showed poor agreement (sensitivity 0.2) due to lack of occipital electrodes. Other sleep parameters, such as sleep latency and rapid eye movement (REM) onset latency, had decreased agreement. Participants with disrupted sleep consolidation, such as from obstructive sleep apnea, also had poor agreement. We suspect that disagreement in sleep parameters between the single-channel EEG and polysomnography is due partially to altered waveform morphology and/or poorer signal quality in the single-channel derivation. Our results show that single-channel EEG provides comparable results to polysomnography in assessing REM, combined Stages N2 and N3 sleep and several other parameters, including frontal slow wave activity. The data establish that single-channel EEG can be a useful research tool.

Overnight changes in waking auditory evoked potential amplitude reflect altered sleep homeostasis in major depression

OBJECTIVE

Sleep homeostasis is altered in major depressive disorder (MDD). Pre- to postsleep decline in waking auditory evoked potential (AEP) amplitude has been correlated with sleep slow wave activity (SWA), suggesting that overnight changes in waking AEP amplitude are homeostatically regulated in healthy individuals. This study investigated whether the overnight change in waking AEP amplitude and its relation to SWA is altered in MDD.

METHOD

Using 256-channel high-density electroencephalography, all-night sleep polysomnography and single-tone waking AEPs pre- and postsleep were collected in 15 healthy controls (HC) and 15 non-medicated individuals with MDD.

RESULTS

N1 and P2 amplitudes of the waking AEP declined after sleep in the HC group, but not in MDD. The reduction in N1 amplitude also correlated with fronto-central SWA in the HC group, but a comparable relationship was not found in MDD, despite equivalent SWA between groups. No pre- to postsleep differences were found for N1 or P2 latencies in either group. These findings were not confounded by varying levels of alertness or differences in sleep variables between groups.

CONCLUSION

MDD involves altered sleep homeostasis as measured by the overnight change in waking AEP amplitude. Future research is required to determine the clinical implications of these findings.

Altered slow wave activity in major depressive disorder with hypersomnia: a high density EEG pilot study

Hypersomnolence in major depressive disorder (MDD) plays an important role in the natural history of the disorder, but the basis of hypersomnia in MDD is poorly understood. Slow wave activity (SWA) has been associated with sleep homeostasis, as well as sleep restoration and maintenance, and may be altered in MDD. Therefore, we conducted a post-hoc study that utilized high density electroencephalography (hdEEG) to test the hypothesis that MDD subjects with hypersomnia (HYS+) would have decreased SWA relative to age- and sex-matched MDD subjects without hypersomnia (HYS-) and healthy controls (n=7 for each group). After correction for multiple comparisons using statistical non-parametric mapping, HYS+ subjects demonstrated significantly reduced parieto-occipital all-night SWA relative to HYS- subjects. Our results suggest hypersomnolence may be associated with topographic reductions in SWA in MDD. Further research using an adequately powered prospective design is indicated to confirm these findings.

Antidepressant effects of selective slow wave sleep deprivation in major depression: a high-density EEG investigation

Sleep deprivation can acutely reverse depressive symptoms in some patients with major depression. Because abnormalities in slow wave sleep are one of the most consistent biological markers of depression, it is plausible that the antidepressant effects of sleep deprivation are due to the effects on slow wave homeostasis. This study tested the prediction that selectively reducing slow waves during sleep (slow wave deprivation; SWD), without disrupting total sleep time, will lead to an acute reduction in depressive symptomatology. As part of a multi-night, cross-over design study, participants with major depression (non-medicated; n = 17) underwent baseline, SWD, and recovery sleep sessions, and were recorded with high-density EEG (hdEEG). During SWD, acoustic stimuli were played to suppress subsequent slow waves, without waking up the participant. The effects of SWD on depressive symptoms were assessed with both self-rated and researcher-administered scales. Participants experienced a significant decrease in depressive symptoms according to both self-rated (p = .007) and researcher-administered (p = .010) scales, while vigilance was unaffected. The reduction in depressive symptoms correlated with the overnight dissipation of fronto-central slow wave activity (SWA) on baseline sleep, the rebound in right frontal all-night SWA on recovery sleep, and the amount of REM sleep on the SWD night. In addition to highlighting the benefits of hdEEG in detecting regional changes in brain activity, these findings suggest that SWD may help to better understand the pathophysiology of depression and may be a useful tool for the neuromodulatory reversal of depressive symptomatology.

Electrophysiological traces of visuomotor learning and their renormalization after sleep

OBJECTIVE

Adapting movements to a visual rotation involves the activation of right posterior parietal areas. Further performance improvement requires an increase of slow wave activity in subsequent sleep in the same areas. Here we ascertained whether a post-learning trace is present in wake EEG and whether such a trace is influenced by sleep slow waves.

METHODS

In two separate sessions, we recorded high-density EEG in 17 healthy subjects before and after a visuomotor rotation task, which was performed both before and after sleep. High-density EEG was recorded also during sleep. One session aimed to suppress sleep slow waves, while the other session served as a control.

RESULTS

After learning, we found a trace in the eyes-open wake EEG as a local, parietal decrease in alpha power. After the control night, this trace returned to baseline levels, but it failed to do so after slow wave deprivation. The overnight change of the trace correlated with the dissipation of low frequency (<8 Hz) NREM sleep activity only in the control session.

CONCLUSIONS

Visuomotor learning leaves a trace in the wake EEG alpha power that appears to be renormalized by sleep slow waves.

SIGNIFICANCE

These findings link visuomotor learning to regional changes in wake EEG and sleep homeostasis.

A postsleep decline in auditory evoked potential amplitude reflects sleep homeostasis

OBJECTIVE

It has been hypothesized that slow wave activity, a well established measure of sleep homeostasis that increases after waking and decreases after sleep, may reflect changes in cortical synaptic strength. If so, the amplitude of sensory evoked responses should also vary as a function of time awake and asleep in a way that reflects sleep homeostasis.

METHODS

Using 256-channel, high-density electroencephalography (EEG) in 12 subjects, auditory evoked potentials (AEP) and spontaneous waking data were collected during wakefulness before and after sleep.

RESULTS

The amplitudes of the N1 and P2 waves of the AEP were reduced after a night of sleep. In addition, the decline in N1 amplitude correlated with low-frequency EEG power during non-rapid eye movement sleep and spontaneous wakefulness, both homeostatically regulated measures of sleep need.

CONCLUSIONS

The decline in AEP amplitude after a night of sleep may reflect a homeostatic reduction in synaptic strength.

SIGNIFICANCE

These findings provide further evidence for a connection between synaptic plasticity and sleep homeostasis.

Propofol anesthesia and sleep: a high-density EEG study

STUDY OBJECTIVES

The electrophysiological correlates of anesthetic sedation remain poorly understood. We used high-density electroencephalography (hd-EEG) and source modeling to investigate the cortical processes underlying propofol anesthesia and compare them to sleep.

DESIGN

256-channel EEG recordings in humans during propofol anesthesia.

SETTING

Hospital operating room.

PATIENTS OR PARTICIPANTS

8 healthy subjects (4 males).

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Initially, propofol induced increases in EEG power from 12-25 Hz. Loss of consciousness (LOC) was accompanied by the appearance of EEG slow waves that resembled the slow waves of NREM sleep. We compared slow waves in propofol to slow waves recorded during natural sleep and found that both populations of waves share similar cortical origins and preferentially propagate along the mesial components of the default network. However, propofol slow waves were spatially blurred compared to sleep slow waves and failed to effectively entrain spindle activity. Propofol also caused an increase in gamma (25-40 Hz) power that persisted throughout LOC. Source modeling analysis showed that this increase in gamma power originated from the anterior and posterior cingulate cortices. During LOC, we found increased gamma functional connectivity between these regions compared to the wakefulness.

CONCLUSIONS

Propofol anesthesia is a sleep-like state and slow waves are associated with diminished consciousness even in the presence of high gamma activity.

Temporal evolution of oscillatory activity predicts performance in a choice-reaction time reaching task

In this study, we characterized the patterns and timing of cortical activation of visually guided movements in a task with critical temporal demands. In particular, we investigated the neural correlates of motor planning and on-line adjustments of reaching movements in a choice-reaction time task. High-density electroencephalography (EEG, 256 electrodes) was recorded in 13 subjects performing reaching movements. The topography of the movement-related spectral perturbation was established across five 250-ms temporal windows (from prestimulus to postmovement) and five frequency bands (from theta to beta). Nine regions of interest were then identified on the scalp, and their activity was correlated with specific behavioral outcomes reflecting motor planning and on-line adjustments. Phase coherence analysis was performed between selected sites. We found that motor planning and on-line adjustments share similar topography in a fronto-parietal network, involving mostly low frequency bands. In addition, activities in the high and low frequency ranges have differential function in the modulation of attention with the former reflecting the prestimulus, top-down processes needed to promote timely responses, and the latter the planning and control of sensory-motor processes.

Sleep-dependent improvement in visuomotor learning: a causal role for slow waves

STUDY OBJECTIVES

Sleep after learning often benefits memory consolidation, but the underlying mechanisms remain unclear. In previous studies, we found that learning a visuomotor task is followed by an increase in sleep slow wave activity (SWA, the electroencephalographic [EEG] power density between 0.5 and 4.5 Hz during non-rapid eye movement sleep) over the right parietal cortex. The SWA increase correlates with the postsleep improvement in visuomotor performance, suggesting that SWA may be causally responsible for the consolidation of visuomotor learning. Here, we tested this hypothesis by studying the effects of slow wave deprivation (SWD).

DESIGN

After learning the task, subjects went to sleep, and acoustic stimuli were timed either to suppress slow waves (SWD) or to interfere as little as possible with spontaneous slow waves (control acoustic stimulation, CAS).

SETTING

Sound-attenuated research room.

PARTICIPANTS

Healthy subjects (mean age 24.6 +/- 1.0 years; n = 9 for EEG analysis, n = 12 for behavior analysis; 3 women).

MEASUREMENTS AND RESULTS

Sleep time and efficiency were not affected, whereas SWA and the number of slow waves decreased in SWD relative to CAS. Relative to the night before, visuomotor performance significantly improved in the CAS condition (+5.93% +/- 0.88%) but not in the SWD condition (-0.77% +/- 1.16%), and the direct CAS vs SWD comparison showed a significant difference (P = 0.0007, n = 12, paired t test). Changes in visuomotor performance after SWD were correlated with SWA changes over right parietal cortex but not with the number of arousals identified using clinically established criteria, nor with any sign of "EEG lightening" identified using a novel automatic method based on event-related spectral perturbation analysis.

CONCLUSION

These results support a causal role for sleep slow waves in sleep-dependent improvement of visuomotor performance.