Perioperative sleep in geriatric cardiac surgical patients: a feasibility study using a wireless wearable device

Investigating the impact of electroconvulsive therapy on brain networks and sleep: an observational study protocol

INTRODUCTION

Electroconvulsive therapy (ECT) is a highly effective treatment for refractory depression, but it may also cause cognitive side effects. Despite decades of use, the mechanisms by which ECT exerts both its antidepressant and cognitive effects are still poorly understood, with the latter substantially limiting referral and adherence to therapy. ECT induces changes in correlated neural activity-functional connectivity-across various brain networks, which may underlie both its clinical efficacy and associated cognitive side effects. Electroencephalography (EEG) could address these knowledge gaps by identifying biomarkers that predict therapeutic outcomes or cognitive side effects. Such developments could ultimately improve patient selection and adherence. Such markers likely span large-scale functional brain networks or temporal dynamics of brain activity during sleep. We hypothesise that enhancement in slow wave sleep mediates the relationship between antidepressant effects and changes in functional connectivity throughout the course of ECT.

METHODS AND ANALYSIS

Disruptions of Brain Networks and Sleep by Electroconvulsive Therapy (DNS-ECT) is an ongoing observational study investigating the impact of ECT on large-scale brain functional networks and their relationships to sleep slow waves, an EEG marker linked to synaptic plasticity. The novelty of this study stems from our focus on the assessment of EEG markers during sleep, wakefulness and ECT-induced seizures over the course of therapy. Graph-based network analyses of high-density EEG signals allow characterisation of functional networks locally in specific subnetworks and globally over large-scale functional networks. Longitudinal assessments of EEG alongside clinical and cognitive outcomes provide a unique opportunity to improve our understanding of the circuit mechanisms underlying the development of cognitive impairments and antidepressant effects incurred during ECT.

ETHICS AND DISSEMINATION

Recruitment for this 5-year study started in March 2023. Dissemination plans include presentations at scientific conferences and peer-reviewed publications. This study has been registered with ClinicalTrials.gov registry under identifier.

TRIAL REGISTRATION NUMBER

NCT05905705.

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

A personalized semi-automatic sleep spindle detection (PSASD) framework

BACKGROUND

Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone.

NEW METHODS

A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components.

RESULTS

A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures.

COMPARISON WITH EXISTING METHODS

PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches.

CONCLUSIONS

Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

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.

Propofol enhancement of slow wave sleep to target the nexus of geriatric depression and cognitive dysfunction: protocol for a phase I open label trial

INTRODUCTION

Late-life treatment-resistant depression (LL-TRD) is common and increases risk for accelerated ageing and cognitive decline. Impaired sleep is common in LL-TRD and is a risk factor for cognitive decline. Slow wave sleep (SWS) has been implicated in key processes including synaptic plasticity and memory. A deficiency in SWS may be a core component of depression pathophysiology. The anaesthetic propofol can induce electroencephalographic (EEG) slow waves that resemble SWS. Propofol may enhance SWS and oral antidepressant therapy, but relationships are unclear. We hypothesise that propofol infusions will enhance SWS and improve depression in older adults with LL-TRD. This hypothesis has been supported by a recent small case series.

METHODS AND ANALYSIS

SWIPED (Slow Wave Induction by Propofol to Eliminate Depression) phase I is an ongoing open-label, single-arm trial that assesses the safety and feasibility of using propofol to enhance SWS in older adults with LL-TRD. The study is enrolling 15 English-speaking adults over age 60 with LL-TRD. Participants will receive two propofol infusions 2-6 days apart. Propofol infusions are individually titrated to maximise the expression of EEG slow waves. Preinfusion and postinfusion sleep architecture are evaluated through at-home overnight EEG recordings acquired using a wireless headband equipped with dry electrodes. Sleep EEG recordings are scored manually. Key EEG measures include sleep slow wave activity, SWS duration and delta sleep ratio. Longitudinal changes in depression, suicidality and anhedonia are assessed. Assessments are performed prior to the first infusion and up to 10 weeks after the second infusion. Cognitive ability is assessed at enrolment and approximately 3 weeks after the second infusion.

ETHICS AND DISSEMINATION

The study was approved by the Washington University Human Research Protection Office. Recruitment began in November 2022. Dissemination plans include presentations at scientific conferences, peer-reviewed publications and mass media. Positive results will lead to a larger phase II randomised placebo-controlled trial.

TRIAL REGISTRATION NUMBER

NCT04680910.

Harnessing technology to improve sleep in frontline healthcare workers: A pilot study of electronic noise-masking earbuds on subjective and objective sleep measures

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen's d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen's d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

The nuts and bolts of multimodal anaesthesia in the 21st century: a primer for clinicians

PURPOSE OF REVIEW

This review article explores the application of multimodal anaesthesia in general anaesthesia, particularly in conjunction with locoregional anaesthesia, specifically focusing on the importance of EEG monitoring. We provide an evidence-based guide for implementing multimodal anaesthesia, encompassing drug combinations, dosages, and EEG monitoring techniques, to ensure reliable intraoperative anaesthesia while minimizing adverse effects and improving patient outcomes.

RECENT FINDINGS

Opioid-free and multimodal general anaesthesia have significantly reduced opioid addiction and chronic postoperative pain. However, the evidence supporting the effectiveness of these approaches is limited. This review attempts to integrate research from broader neuroscientific fields to generate new clinical hypotheses. It discusses the correlation between high-dose intraoperative opioids and increased postoperative opioid consumption and their impact on pain indices and readmission rates. Additionally, it explores the relationship between multimodal anaesthesia and pain processing models and investigates the potential effects of nonpharmacological interventions on preoperative anxiety and postoperative pain.

SUMMARY

The integration of EEG monitoring is crucial for guiding adequate multimodal anaesthesia and preventing excessive anaesthesia dosing. Furthermore, the review investigates the impact of combining regional and opioid-sparing general anaesthesia on perioperative EEG readings and anaesthetic depth. The findings have significant implications for clinical practice in optimizing multimodal anaesthesia techniques (Supplementary Digital Content 1: Video Abstract, http://links.lww.com/COAN/A96 ).

Resting-state prefrontal EEG biomarker in correlation with postoperative delirium in elderly patients

Postoperative delirium (POD) is associated with adverse outcomes in elderly patients after surgery. Electroencephalography (EEG) can be used to develop a potential biomarker for degenerative cerebral dysfunctions, including mild cognitive impairment and dementia. This study aimed to explore the relationship between preoperative EEG and POD. We included 257 patients aged >70 years who underwent spinal surgery. We measured the median dominant frequency (MDF), which is a resting-state EEG biomarker involving intrinsic alpha oscillations that reflect an idle cortical state, from the prefrontal regions. Additionally, the mini-mental state examination and Montreal cognitive assessment (MoCA) were performed before surgery as well as 5 days after surgery. For long-term cognitive function follow up, the telephone interview for cognitive status™ (TICS) was performed 1 month and 1 year after surgery. Fifty-two (20.2%) patients were diagnosed with POD. A multivariable logistic regression analysis that included age, MoCA score, Charlson comorbidity index score, Mini Nutritional Assessment, and the MDF as variables revealed that the MDF had a significant odds ratio of 0.48 (95% confidence interval 0.27-0.85). Among the patients with POD, the postoperative neurocognitive disorders could last up to 1 year. Low MDF on preoperative EEG was associated with POD in elderly patients undergoing surgery. EEG could be a novel potential tool for identifying patients at a high risk of POD.

Targeting Slow Wave Sleep Deficiency in Late-Life Depression: A Case Series With Propofol

Slow wave sleep (SWS), characterized by large electroencephalographic oscillations, facilitates crucial physiologic processes that maintain synaptic plasticity and overall brain health. Deficiency in older adults is associated with depression and cognitive dysfunction, such that enhancing sleep slow waves has emerged as a promising target for novel therapies. Enhancement of SWS has been noted after infusions of propofol, a commonly used anesthetic that induces electroencephalographic patterns resembling non-rapid eye movement sleep. This paper 1) reviews the scientific premise underlying the hypothesis that sleep slow waves are a novel therapeutic target for improving cognitive and psychiatric outcomes in older adults, and 2) presents a case series of two patients with late-life depression who each received two propofol infusions. One participant, a 71-year-old woman, had a mean of 2.8 minutes of evening SWS prior to infusions (0.7% of total sleep time). SWS increased on the night after each infusion, to 12.5 minutes (5.3% of total sleep time) and 24 minutes (10.6% of total sleep time), respectively. Her depression symptoms improved, reflected by a reduction in her Montgomery-Asberg Depression Rating Scale (MADRS) score from 26 to 7. In contrast, the other participant, a 77-year-old man, exhibited no SWS at baseline and only modest enhancement after the second infusion (3 minutes, 1.3% of total sleep time). His MADRS score increased from 13 to 19, indicating a lack of improvement in his depression. These cases provide proof-of-concept that propofol can enhance SWS and improve depression for some individuals, motivating an ongoing clinical trial (ClinicalTrials.gov NCT04680910).

ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks

Changes in the intracellular calcium concentration are a fundamental fingerprint of astrocytes, the main type of glial cell. Astrocyte calcium signals can be measured with two-photon microscopy, occur in anatomically restricted subcellular regions, and are coordinated across astrocytic networks. However, current analytical tools to identify the astrocytic subcellular regions where calcium signals occur are time-consuming and extensively rely on user-defined parameters. These limitations limit reproducibility and prevent scalability to large datasets and fields-of-view. Here, we present Astrocytic calcium Spatio-Temporal Rapid Analysis (ASTRA), a novel software combining deep learning with image feature engineering for fast and fully automated semantic segmentation of two-photon calcium imaging recordings of astrocytes. We applied ASTRA to several two-photon microscopy datasets and found that ASTRA performed rapid detection and segmentation of astrocytic cell somata and processes with performance close to that of human experts, outperformed state-of-the-art algorithms for the analysis of astrocytic and neuronal calcium data, and generalized across indicators and acquisition parameters. We also applied ASTRA to the first report of two-photon mesoscopic imaging of hundreds of astrocytes in awake mice, documenting large-scale redundant and synergistic interactions in extended astrocytic networks. ASTRA is a powerful tool enabling closed-loop and large-scale reproducible investigation of astrocytic morphology and function.

Association of sleep and anaesthesia EEG biomarkers with preoperative MoCA score: A pilot study

INTRODUCTION

Preoperative cognitive impairments increase the risk of postoperative complications. The electroencephalogram (EEG) could provide information on cognitive vulnerability. The feasibility and clinical relevance of sleep EEG (EEG_sleep ) compared to intraoperative EEG (EEG_intraop ) in cognitive risk stratification remains to be explored. We investigated similarities between EEG_sleep and EEG_intraop vis-a-vis preoperative cognitive impairments.

METHODS

Pilot study including 27 patients (63 year old [53.5, 70.0]) to whom Montreal cognitive assessment (MoCA) and EEG_sleep were administered 1 day before a propofol-based general anaesthesia, in addition to EEG_intraop acquisition from depth-of-anaesthesia monitors. Sleep spindles on EEG_sleep and intraoperative alpha-band power on EEG_intraop were particularly explored.

RESULTS

In total, 11 (41%) patients had a MoCA <25 points. These patients had a significantly lower sleep spindle power on EEG_sleep (25 vs. 40 μv² /Hz, p = .035) and had a weaker intraoperative alpha-band power on EEG_intraop (85 vs. 150 μv² /Hz, p = .001) compared to patients with normal MoCA. Correlation between sleep spindle and intraoperative alpha-band power was positive and significant (r = 0.544, p = .003).

CONCLUSION

Preoperative cognitive impairment appears to be detectable by both EEG_sleep and EEG_intraop . Preoperative sleep EEG to assess perioperative cognitive risk is feasible but more data are needed to demonstrate its benefit compared to intraoperative EEG.

Postoperative Delirium Severity and Recovery Correlate With Electroencephalogram Spectral Features

BACKGROUND

Delirium is an acute syndrome characterized by inattention, disorganized thinking, and an altered level of consciousness. A reliable biomarker for tracking delirium does not exist, but oscillations in the electroencephalogram (EEG) could address this need. We evaluated whether the frequencies of EEG oscillations are associated with delirium onset, severity, and recovery in the postoperative period.

METHODS

Twenty-six adults enrolled in the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES; ClinicalTrials.gov NCT02241655) study underwent major surgery requiring general anesthesia, and provided longitudinal postoperative EEG recordings for this prespecified substudy. The presence and severity of delirium were evaluated with the confusion assessment method (CAM) or the CAM-intensive care unit. EEG data obtained during awake eyes-open and eyes-closed states yielded relative power in the delta (1-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. Discriminability for delirium presence was evaluated with c-statistics. To account for correlation among repeated measures within patients, mixed-effects models were generated to assess relationships between: (1) delirium severity and EEG relative power (ordinal), and (2) EEG relative power and time (linear). Slopes of ordinal and linear mixed-effects models are reported as the change in delirium severity score/change in EEG relative power, and the change in EEG relative power/time (days), respectively. Bonferroni correction was applied to confidence intervals (CIs) to account for multiple comparisons.

RESULTS

Occipital alpha relative power during eyes-closed states offered moderate discriminability (c-statistic, 0.75; 98% CI, 0.58-0.87), varying inversely with delirium severity (slope, -0.67; 98% CI, -1.36 to -0.01; P = .01) and with severity of inattention (slope, -1.44; 98% CI, -2.30 to -0.58; P = .002). Occipital theta relative power during eyes-open states correlated directly with severity of delirium (slope, 1.28; 98% CI, 0.12-2.44; P = .007), inattention (slope, 2.00; 98% CI, 0.48-3.54; P = .01), and disorganized thinking (slope, 3.15; 98% CI, 0.66-5.65; P = .01). Corresponding frontal EEG measures recapitulated these relationships to varying degrees. Severity of altered level of consciousness correlated with frontal theta relative power during eyes-open states (slope, 11.52; 98% CI, 6.33-16.71; P < .001). Frontal theta relative power during eyes-open states correlated inversely with time (slope, -0.05; 98% CI, -0.12 to -0.04; P = .002).

CONCLUSIONS

Presence, severity, and core features of postoperative delirium covary with spectral features of the EEG. The cost and accessibility of EEG facilitate the translation of these findings to future mechanistic and interventional trials.

Correlating electroconvulsive therapy response to electroencephalographic markers: Study protocol

Introduction

Electroconvulsive therapy (ECT) is an effective intervention for patients with major depressive disorder (MDD). Despite longstanding use, the underlying mechanisms of ECT are unknown, and there are no objective prognostic biomarkers that are routinely used for ECT response. Two electroencephalographic (EEG) markers, sleep slow waves and sleep spindles, could address these needs. Both sleep microstructure EEG markers are associated with synaptic plasticity, implicated in memory consolidation, and have reduced expression in depressed individuals. We hypothesize that ECT alleviates depression through enhanced expression of sleep slow waves and sleep spindles, thereby facilitating synaptic reconfiguration in pathologic neural circuits.

Methods

Correlating ECT Response to EEG Markers (CET-REM) is a single-center, prospective, observational investigation. Wireless wearable headbands with dry EEG electrodes will be utilized for at-home unattended sleep studies to allow calculation of quantitative measures of sleep slow waves (EEG SWA, 0.5-4 Hz power) and sleep spindles (density in number/minute). High-density EEG data will be acquired during ECT to quantify seizure markers.

Discussion

This innovative study focuses on the longitudinal relationships of sleep microstructure and ECT seizure markers over the treatment course. We anticipate that the results from this study will improve our understanding of ECT.