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.

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.

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-ß42 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-ß42 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.

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.

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.

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 κ 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 held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ 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 research.

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.

Sleep deprivation exacerbates microglial reactivity and Aß deposition in a TREM2-dependent manner in mice.

Sleep loss is associated with cognitive decline in the aging population and is a risk factor for Alzheimer's disease (AD). Considering the crucial role of immunomodulating genes such as that encoding the triggering receptor expressed on myeloid cells type 2 (TREM2) in removing pathogenic amyloid-ß (Aß) plaques and regulating neurodegeneration in the brain, our aim was to investigate whether and how sleep loss influences microglial function in mice. We chronically sleep-deprived wild-type mice and the 5xFAD mouse model of cerebral amyloidosis, expressing either the humanized TREM2 common variant, the loss-of-function R47H AD-associated risk variant, or without TREM2 expression. Sleep deprivation not only enhanced TREM2-dependent Aß plaque deposition compared with 5xFAD mice with normal sleeping patterns but also induced microglial reactivity that was independent of the presence of parenchymal Aß plaques. We investigated lysosomal morphology using transmission electron microscopy and found abnormalities particularly in mice without Aß plaques and also observed lysosomal maturation impairments in a TREM2-dependent manner in both microglia and neurons, suggesting that changes in sleep modified neuro-immune cross-talk. Unbiased transcriptome and proteome profiling provided mechanistic insights into functional pathways triggered by sleep deprivation that were unique to TREM2 and Aß pathology and that converged on metabolic dyshomeostasis. Our findings highlight that sleep deprivation directly affects microglial reactivity, for which TREM2 is required, by altering the metabolic ability to cope with the energy demands of prolonged wakefulness, leading to further Aß deposition, and underlines the importance of sleep modulation as a promising future therapeutic approach.

Modulation of gamma and theta spectral amplitude and phase synchronization is associated with the development of visuo-motor learning.

The formation of new motor memories, which is fundamental for efficient performance during adaptation to a visuo-motor rotation, occurs when accurate planning is achieved mostly with feedforward mechanisms. The dynamics of brain activity underlying the switch from feedback to feedforward control is still matter of debate. Based on the results of studies in declarative learning, it is likely that phase synchronization of low and high frequencies as well as their temporal modulation in power amplitude underlie the formation of new motor memories during visuo-motor adaptation. High-density EEG (256 electrodes) was recorded in 17 normal human subjects during adaptation to a visuo-motor rotation of 60° in four incremental steps of 15°. We found that initial learning is associated with enhancement of gamma power in a right parietal region during movement execution as well as gamma/theta phase coherence during movement planning. Late stages of learning are instead accompanied by an increase of theta power over that same right parietal region during movement planning, which is correlated with the degree of learning and retention. Altogether, these results suggest that the formation of new motor memories and, thus, the switch from feedback to feedforward control is associated with the modulation of gamma and theta spectral activities, with respect to their amplitude and phase, during movement planning and execution. Specifically, we propose that gamma/theta phase coupling plays a pivotal role in the integration of a new representation into motor memories.

Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state.

The existence of normal sleep in patients in a vegetative state is still a matter of debate. Previous electrophysiological sleep studies in patients with disorders of consciousness did not differentiate patients in a vegetative state from patients in a minimally conscious state. Using high-density electroencephalographic sleep recordings, 11 patients with disorders of consciousness (six in a minimally conscious state, five in a vegetative state) were studied to correlate the electrophysiological changes associated with sleep to behavioural changes in vigilance (sustained eye closure and muscle inactivity). All minimally conscious patients showed clear electroencephalographic changes associated with decreases in behavioural vigilance. In the five minimally conscious patients showing sustained behavioural sleep periods, we identified several electrophysiological characteristics typical of normal sleep. In particular, all minimally conscious patients showed an alternating non-rapid eye movement/rapid eye movement sleep pattern and a homoeostatic decline of electroencephalographic slow wave activity through the night. In contrast, for most patients in a vegetative state, while preserved behavioural sleep was observed, the electroencephalographic patterns remained virtually unchanged during periods with the eyes closed compared to periods of behavioural wakefulness (eyes open and muscle activity). No slow wave sleep or rapid eye movement sleep stages could be identified and no homoeostatic regulation of sleep-related slow wave activity was observed over the night-time period. In conclusion, we observed behavioural, but no electrophysiological, sleep wake patterns in patients in a vegetative state, while there were near-to-normal patterns of sleep in patients in a minimally conscious state. These results shed light on the relationship between sleep electrophysiology and the level of consciousness in severely brain-damaged patients. We suggest that the study of sleep and homoeostatic regulation of slow wave activity may provide a complementary tool for the assessment of brain function in minimally conscious state and vegetative state patients.

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.

Sex-related differences in sleep slow wave activity in major depressive disorder: a high-density EEG investigation.

BACKGROUND: Sleep disturbance plays an important role in major depressive disorder (MDD). Prior investigations have demonstrated that slow wave activity (SWA) during sleep is altered in MDD; however, results have not been consistent across studies, which may be due in part to sex-related differences in SWA and/or limited spatial resolution of spectral analyses. This study sought to characterize SWA in MDD utilizing high-density electroencephalography (hdEEG) to examine the topography of SWA across the cortex in MDD, as well as sex-related variation in SWA topography in the disorder. METHODS: All-night recordings with 256 channel hdEEG were collected in 30 unipolar MDD subjects (19 women) and 30 age and sex-matched control subjects. Spectral analyses of SWA were performed to determine group differences. SWA was compared between MDD and controls, including analyses stratified by sex, using statistical non-parametric mapping to correct for multiple comparisons of topographic data. RESULTS: As a group, MDD subjects demonstrated significant increases in all-night SWA primarily in bilateral prefrontal channels. When stratified by sex, MDD women demonstrated global increases in SWA relative to age-matched controls that were most consistent in bilateral prefrontal regions; however, MDD men showed no significant differences relative to age-matched controls. Further analyses demonstrated increased SWA in MDD women was most prominent in the first portion of the night. CONCLUSIONS: Women, but not men with MDD demonstrate significant increases in SWA in multiple cortical areas relative to control subjects. Further research is warranted to investigate the role of SWA in MDD, and to clarify how increased SWA in women with MDD is related to the pathophysiology of the disorder.

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.

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.

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.

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.

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.

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.