bluuebunny/crossref_metadata_2025_split huggingface dataset:
0277 Robust Identification of Sleep States and Their Transitions Using Whole-Night fMRI Data
Authors: Yang Nils , Picchioni Dante , de Zwart Jacco , Wang Yicun , van Gelderen Peter , Duyn Jeff 
Published: April 2024

Abstract

Introduction
Polysomnography (PSG)-based sleep staging has a relatively poor (30s) temporal resolution, and as a result does not capture any sleep structure that may exist on finer time-scales. For example, brain activity patterns involved with transitions or changes within and between classical sleep stages are not captured. Here we used whole-night functional magnetic resonance imaging (fMRI) to address this shortcoming.

Methods
Data acquisition was conducted as part of a well-controlled sleep experiment with two consecutive nights of concurrent fMRI-EEG recording from 23:00 to 7:00. Twelve participants (18-35 years old, 8 females) had at least one complete sleep cycle during both Night 1 and 2. We analyzed the fMRI data using an unsupervised learning approach - Hidden Markov Model (HMM) to reveal recurrent structure (“states”). The HMM, applied to Night 2 data, encompassed 21 whole-brain states based on various model evaluation indexes. Each state was characterized as a multivariate Gaussian distribution including a mean fMRI activation distribution,a functional connectivity (FC) matrix, and a transition probability matrix.

Results
Modular analysis of the extracted transition probability matrix (between each pair of states) revealed five modules, each containing a set of states that had similar preferences to occur during a specific sleep stage or part thereof. Remarkably, the trained HMM predicted brain sleep states during Night 1 that had highly similar sleep stage preference (r = 0.94, p &lt; 0.001). In Wake-related HMM states, the FCs between DMN and Salience Network (SAL) / Control Network (CON) were negative, while during N3-related HMM states, these FCs were positive. Similar patterns were found for mean fMRI activation.

Conclusion
These observations suggest that HMM may provide valuable information about sleep-state-specific brain activity patterns that extend well beyond the information provided by classical sleep staging. For example, cursory inspection of the HMM results suggests a subdivision of N2 in two groups of multiple states with different transition probabilities to Wake; analysis of brain activation and FC patterns of HMM states indicated that the connections between DMN and SAL/CON may play a critical role in the transition from wake to light sleep and, to deep sleep.

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0280 Effect of Daily Transcutaneous Vagal Nerve Stimulation on Objective and Subjective Sleep Features in Healthy Adults
Authors: Subramoniam Anjana , Silva-Jones Jillian , Crosswell Alexandra , Whitehurst Lauren 
Published: April 2024

Abstract

Introduction
One key pathway through which the autonomic nervous system modulates sleep is via the vagus nerve. No study to date has examined the impact of daily vagal modulation on nightly sleep activity. Here, in a two-week, within-subject sham-controlled and counterbalanced study, we investigated the impact of daily, non-invasive vagal nerve stimulation on subjective and objective sleep features.

Methods
The Institutional Review Board of the University of Kentucky approved experimental procedures. Twenty healthy adults, aged between 18-65, were given ambulatory vagal nerve stimulators to wear at home for one week of active and one week of sham stimulation. Stimulation conditions were counterbalanced across participants. The device was worn for 15 minutes between 9 PM-11 PM each day before bedtime. Before and after sleep, participants completed the Karolinska Sleepiness Scale (KSS) to measure subjective sleepiness and completed daily diaries to gather subjective sleep outcomes. Participants also wore wrist actigraphs to objectively characterize behavioral sleep/wake patterns. Linear mixed effect models assessed the effect of tVNS on subjective and objective measures of sleep.

Results
tVNS had a significant impact on actigraphy indices of sleep behavior. Compared to sham stimulation nights, there was a significant reduction in wake after sleep onset (b=-17.12, p=.004), a significant improvement in amount of time spent asleep during the night (b=2.87, p=.01) and higher sleep efficiencies (b = 2.05, p=.01) on stimulation nights. No significant differences were found in KSS ratings before and after sleep or in subjective sleep assessments measured with daily diaries.

Conclusion
The results suggest that the use of daily tVNS may improve behavioral indices of sleep, highlighting the important role that autonomic activity may play in regulating nighttime sleep/wake function. Future studies should examine tVNS impacts on polysomnographically-measured sleep

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0282 Associations Between EEG Sleep Characteristics and Alcohol Use Behaviors in Young Adults
Authors: Harris Nicholas , Oryshkewych Nina , Buysse Daniel , Pedersen Sarah , Wallace Meredith , Hasler Brant 
Published: April 2024

Abstract

Introduction
Alcohol use disorder (AUD) is characterized by sleep disturbances, but differentiating the effects of acute alcohol exposure, withdrawal, and chronic consumption remains an unsolved problem. This study takes a unique approach to examine proximate alcohol consumption effects on sleep electroencephalography (EEG) in young adults who engage in heavy episodic drinking. We aim to relate sleep physiology to progression from binge drinking towards AUD.

Methods
Young adults aged 21-30 years (N=88) who report weekly binge drinking (4+/5+ drinks per occasion for female/male assigned at birth) completed two nine-day ecological momentary assessment (EMA) protocols, each followed by overnight in-lab polysomnography (PSG) after negative alcohol breath test. Drinking events were recorded by EMA. EEG sleep analyses (AASM criteria) assessed sleep-wake staging and power spectral analysis. Linear mixed effect modeling examined the effects of drink number during the EMA period on measures of sleep continuity, architecture, and spectral power, adjusting for sociodemographic and behavioral covariates.

Results
Participants reported an average of 17.5+/-1.0 drinks over 3.0+/-0.1 days in each 9-day EMA period. Unexpectedly, higher drink counts were associated with lower time spent awake after sleep onset (WASO, log-transformed, BETA-STD=-0.17, p=0.028) without significant changes in sleep efficiency (p=0.06) or sleep onset latency (SOL, p=0.656). More drinks also correlated with higher number of REM periods (log-transformed, BETA-STD=0.17, p=0.022) and less REM fragmentation (log-transformed, BETA-STD=-0.21, p=0.005) without correlating with REM latency (p=0.058). Finally, consumption of more drinks was associated with decreased alpha power (8-12 Hz, log-transformed, BETA-STD=-0.1, p=0.03) but not associated with other aspects of sleep architecture or spectral power (p&gt;0.05).

Conclusion
Contrary to our expectation of worse sleep in those with higher drinking frequency, we found that greater drink consumption was actually associated with better sleep based on parameters assessing continuity (WASO), REM sleep (number of periods, fragmentation), and spectral power (alpha power). Possible explanations include effects from binge drinking pattern, sleep improvement after alcohol detoxification, age-related resilience, or differential effects across the night. Future analyses will investigate sex differences, day-to-day drinking and sleeping relationships during EMA study period, subjective sleep quality, sleep changes across the night, and other putative covariates.

Support (if any)
NAH T32HL082610, BPH/SLP R01AA026249
0283 Urinary Thallium Concentration and Objective Sleep: An Examination in Multi-Ethnic Study of Atherosclerosis (MESA)
Authors: Paquet Caitlin , Bertisch Suzanne , Glabonjat Ronald , Sanchez Tiffany , Domingo-Relloso Arce , Navas-Acien Ana , Schilling Kathrin , Purcell Shaun M · , Redline Susan 
Published: April 2024

Abstract

Introduction
Thallium (Tl), a toxic metal abundant in the Earth’s crust, is a neurotoxicant that crosses the blood brain barrier and disrupts potassium-dependent neural communication. Tl’s neurotoxic effects have been linked to sleep disturbance. Prior research on the relationship between Tl and sleep relied on self-reported sleep measures or were isolated to specific populations (e.g., occupational, regional). We examined the relationship between Tl and sleep by analyzing the long-term impacts of Tl exposure on actigraphy- and polysomnography (PSG)-derived sleep outcomes in a large multiethnic cohort across the United States.

Methods
We used data from Multi-Ethnic Study of Atherosclerosis (MESA). Spot urinary measurements of Tl concentration were measured at Exam 1 (2000-2002) and overnight in-home PSG with seven days of wrist-worn actigraphy were collected between 2010-2013. We employed nonparametric partial correlations to examine the relationship between log-transformed Tl and sleep outcomes, adjusting for demographics, site and urinary creatinine.

Results
1822 adults were included (mean age 59.3 (45-84) years, 53.5% female, and 37.5% White, 27.9% Black/African American, 23.3% Hispanic, and 11.2% Chinese). Higher Tl levels were associated with actigraphy-based shorter sleep duration (rs = -.05, p &lt; .05), higher wake after sleep onset (rs = .07, p &lt;.01), shorter sleep maintenance efficiency (rs = -.08, p &lt; .01), and higher activity during sleep (L5, rs = .05, p &lt; .05). In contrast, higher Tl was related to favorable PSG-derived metrics: less %N1 (rs = -.05, p &lt; .05), greater N3 (rs = .06, p &lt; .05), and greater REM duration (rs = .06, p &lt; .05). Higher Tl also was associated with higher sleep regularity (lower sleep duration SD; rs = -.06, p &lt; .01; and Inter-day Stability: rs = .05, p &lt; .05).

Conclusion
Tl was associated with more fragmented sleep measured at home with actigraphy, but with higher sleep regularity, and deeper sleep by PSG. The discordance of associations across sleep domains may indicate a complex effect of metal exposure and requires further investigation. Such findings may contribute to understanding the potential role of heavy metals on neurological process and sleep.

Support (if any)
The MESA Sleep studies were supported by NHLBI HL56984 and NIA R01AG070867.
0285 Measuring Sleep with the Apple Watch: A Comparison of a Machine Learning versus Traditional Algorithms to Actiwatch
Authors: Caputo Dominick , Hirata Mika , Moreno Jennette , Drake Christopher , Walch Olivia , Cheng Philip 
Published: April 2024

Abstract

Introduction
Consumer-based actigraphy has seen rapid adoption in the United States and presents an underutilized opportunity and resource for ecologically valid sleep-wake monitoring. Furthermore, with the discontinuation of Philips Actiwatch, a reliable and scalable alternative is required. Apple Watch may be a promising solution, with prior data indicating strong concordance for activity counts derived from the Apple Watch compared to the Actiwtach. The present study extends previous findings to sleep periods as an outcome of interest.

Methods
A community sample of 40 adults wore an Actiwatch and Apple Watch for 7 to 14 days with daily completion of the consensus sleep diary. Sleep periods from both wrist-worn devices were calculated and compared with sleep periods reported on the sleep diary. Sleep based on Apple Watch was derived using two approaches: one that mirrored traditional actigraphy (ie, sleep-wake classification using the Cole-Kripke model), and another than utilized machine learning with steps and heart rate as inputs. Agreement of sleep periods between the wrist-worn device and the sleep diary was operationalized as percent overlap. Performance of Apple Watch compared to the Actiwatch was then evaluated using the ratio of percentage overlap with sleep diary. A ratio of 1.00 represents perfect agreement.

Results
The agreement between Apple Watch derived sleep periods and sleep diary was comparable to that with Actiwatches. The ratio of percentage overlap between the two devices was 1.00 when using the machine learning algorithm, and 0.97 when using the traditional actigraphy approach. When averaged across the sample, sleep periods derived from Actiwatch overlapped by 80.7% (95% CI [84.1% - 77.3%]) with sleep diary periods, and sleep periods derived from Apple Watch overlapped by 82.0% (95% CI [84.7% - 79.4%]) and 81.2% (95% CI [84.9% - 77.5%]) for the machine learning and traditional actigraphy approaches, respectively. Total sleep period from the Apple Watch produced lower mean absolute errors compared to Actiwatch (machine learning: 24.8 minutes, traditional actigraphy approach: 19.1 minutes).

Conclusion
Sleep periods derived from Apple Watch data showed strong agreement with Actiwatch data, with the machine learning algorithm showing slightly stronger performance compared to the Cole-Kripke algorithm.

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0287 SAFTEr: An R Package for the SAFTE Model
Authors: Choynowski Jake , Devine Jaime , Hursh Steven 
Published: April 2024

Abstract

Introduction
The Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model is a framework that predicts individual performance changes based on variations in circadian phase and sleep/wake schedules. The SAFTE model is used by industries in which fatigue risk is relevant to safety through licensed software like SAFTE-FAST. The SAFTE model may provide helpful context during the analysis of objective and performance sleep data but a full software license may be cost-prohibitive. SAFTEr was created as an open-source R package to allow the use of the original patented SAFTE model as a freely available research tool.

Methods
The SAFTE algorithm was rebuilt from patent equations in collaboration with SAFTE inventor Steven Hursh using the R-coding language. The R package was broken down into three primary steps: 1) formatting data into 1-minute epochs; 2) fitting SAFTE model predictions to the data; and 3) graphing the dataset. SAFTEr includes 17 constants identified in the model and 20 output variables that can be computed on an epoch-by-epoch basis. SAFTEr is best suited for schedules that do not induce a phase change, because the simplified model does not detect and automatically shift circadian phase.

Results
The current version of the SAFTEr package (0.1.2) consists of six functions that model and graph time-based datasets of sleep/wake data and include the ability to customize bedtimes, study start and end times, and events markers. The six functions include: 1) formatting data; 2) identification of possible missing data; 3) modeling; 4) generation of individual graphs with graphic overlay of event markers against the modeled data; 5) graph overlay to compare variables of interest against the SAFTE model as a line chart or; 6) as a scatter plot.

Conclusion
The SAFTEr package is freely available for use by researchers who wish to predict cognitive performance as a function of objective sleep data. The SAFTEr package is an open-source recreation of the SAFTE model that gives anyone with a basic knowledge of R the ability to apply the model to their own data as well as graph it against other measurable tests.

Support (if any)
N/A
0290 Sex Differences: Nicotine Use and Abstinence Effects on Sleep Architecture in Female and Male C57BL/6J Mice
Authors: Aki Sidney , Brown Morgan , Stitzel Jerry , Mathews Hunter 
Published: April 2024

Abstract

Introduction
Nicotine withdrawal symptoms play a major role in relapse rates, and sleep disturbances occurring during withdrawal are a reliable predictor of relapse. Additionally, research has shown that both nicotine withdrawal symptoms and sleep disturbances are sex dependent. Thus, understanding sex differences in sleep architecture during abstinence is critical for treating nicotine addiction. This study aims to investigate sex differences in sleep architecture in female and male C57BL/6J mice undergoing nicotine abstinence.

Methods
EEG/EMG implantation devices were surgically implanted to capture sleep data (n = 9 males, n = 11 females). Mice were individually housed in a 12-hour light/dark schedule. During pre-nicotine baseline, mice received a vehicle solution containing 0.2% saccharin in water. To induce nicotine dependence, 200 μg/ml nicotine was added to the saccharin solution and oral nicotine exposure occurred for 14 days. Abstinence was initiated by removing nicotine from the drinking solution after the 14 day exposure. EEG/EMG was scored for 2 days during baseline and averaged and scored for 4 days during oral nicotine exposure and averaged. EEG/EMG for days 1, 2, and 5 were analyzed individually to assess the duration of any sleep disruptions. Sirenia software was used to determine sleep stages in 4 second epochs. A repeated measures ANOVA test was used along with Dunnett’s post-hoc test.

Results
Nicotine exposure reduced total sleep and NREM time in both females and males during 24-Hour and active phase measures. Abstinence induced a sleep fragmentation phenotype, as defined by an increase in sleep bout frequency with a decrease in sleep bout duration, during the inactive phase in both females and males –. Females had a much more robust sleep fragmentation. Females also exhibited NREM fragmentation.

Conclusion
Nicotine exposure has similar effects to total sleep time and NREM percent time during the 24—hour and active phase measures regardless of sex. Female mice experienced a much robust sleep fragmentation phenotype compared to male mice. NREM fragmentation, which underlined the greater sleep fragmentation phenotype, was unique to female mice.

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0291 Evaluating Sleep Quality Metrics Using Zero-Effort Technology: Implications for Public Health Dynamics
Authors: Kaur Jasleen , Oetomo Arlene , Chauhan Vivek , Morita Plinio 
Published: April 2024

Abstract

Introduction
Sleep quality is critical to human health and well-being, with implications for manifold physiological and psychological processes. The quality and reliability of the data due to recall bias and subjective interpretation often limit traditional methods of sleep data collection. This research presents a novel framework that can objectively measure and evaluate sleep quality using smart thermostats equipped with motion sensors, providing non-invasive and effortless sleep monitoring.

Methods
We leveraged the ecobee 'Donate Your Data' initiative, which collects data from smart home sensors, to analyze 8 Terabytes of data from 178,706 households. In our time-series model, sensor activation values were transformed into signals to model sleep features. We developed a data pipeline integrating data preprocessing, feature engineering, and various machine learning models. These models, such as RNN, VAE, K-means clustering, PCA, and Random Forest classifiers, were used to discern sleep quality indicators. These indicators included Absolute Sleep Duration, Normalized Disturbance Time, Wakeup Onset and Sleep Onset Time, Time in Room, and Sleep Efficiency (with and without onset time) derived from motion sensor data.

Results
Our findings show three distinct sleep quality clusters, with clear variations in sleep duration, disturbances, and efficiency. Cluster 0 profiled a pattern of fewer disturbances and higher sleep efficiency, whereas Cluster 1 indicated a moderate disturbance with prolonged sleep onset and wakeup duration. The lowest average sleep duration and varied disturbances characterized Cluster 2. Comparative analysis underscores the heterogeneity in sleep quality, highlighting the potential of Internet of Things (IoT) devices in identifying sleep patterns and contributing to sleep research without invasive monitoring. These clusters demonstrate the heterogeneity of sleep quality and showcase the potential of Internet of Things (IoT) devices in sleep pattern identification.

Conclusion
This novel approach shows smart thermostats as a viable data source for evaluating sleep quality, providing a new perspective on technology in health surveillance and paving the way for personalized sleep improvement strategies for sleep technologists and healthcare policymakers. Furthermore, integrating temperature data from ecobee datasets could deepen insights into the connection between indoor temperature and sleep, impacting health monitoring and policy in the context of increasing global temperatures from climate change.

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0293 Evaluation of a Single Point of Contact Chest-worn HSAT with ECG-based Atrial Fibrillation Detection
Authors: Goldstein Cathy , Ghanbari Hamid , Sharma Surina , Collop Nancy , Loring Zak , Walsh Colleen , Herreshoff Emily , Pollock Mark , Frankel David , Rosen Ilene 
Published: April 2024

Abstract

Introduction
Obstructive sleep apnea (OSA) is highly prevalent, underdiagnosed, and often comorbid with cardiac arrhythmias such as atrial fibrillation (AF). Existing home sleep apnea tests (HSATs) remain complex, often requiring multiple points of contact, and do not provide gold-standard ECG-based arrhythmia detection. A simplified HSAT with expanded capabilities for ECG-based arrhythmia detection could bridge this diagnostic gap, stimulate sleep-cardiology collaborations, and reduce patient burden; resulting in more comprehensive, patient-centered care. Here we performed a comparative PSG evaluation of the Sansa device (Huxley Medical, Inc.), a single point of contact HSAT worn on the chest that provides heart rate, oximetry, respiratory effort, total sleep time (TST), and ECG.

Methods
Patients undergoing PSG for suspected OSA were enrolled at three sleep laboratories. The chest-worn HSAT was used during overnight, level I PSG. Manually scored PSG (AASM manual 3.0) parameters were compared to the chest-worn HSAT’s associated parameters for AHI requiring hypopneas with 4% oxygen desaturation (sAHI4%), AHI requiring hypopneas with 3% oxygen desaturations (sAHI3%), TST, and classification of moderate-to-severe OSA (AHI≥15). AF and premature ventricular complexes (PVC) were visually compared between ECG derived from PSG and chest-worn HSAT.

Results
Enrollment included 101 patients (47% male, age 52±18 years, BMI 32±9 kg/m2, 27% Fitzpatrick skin tone 5-6). The chest-worn HSAT detected moderate-to-severe OSA with 90% sensitivity and 92% specificity (AHI4%, 37% prevalence) and 88% sensitivity and 80% specificity (AHI3%, 45% prevalence). Strong agreement was demonstrated for sAHI4% (-15.9 to 14.5 95% limits of agreement, LoA), sAHI3% (-23.6 to 20.7 95%-LoA), and TST (-62.0 to 87.7 95%-LoA). Preliminary visual review of ECG showed AF and/or PVCs in at least 15% of overnight recordings, in agreement with corresponding PSG ECG.

Conclusion
Non-flow-based HSATs to assess OSA are increasingly available; however, here we present the performance of a novel device to simultaneously diagnose OSA and AF (and other arrhythmias). Given the relevance of OSA treatment in successful AF management, these data highlight the capacity of a single point of contact chest-worn HSAT in the simultaneous diagnosis of both disorders toward downstream benefits of hastening OSA treatment to improve AF outcomes.

Support (if any)
National Science Foundation; Georgia Research Alliance; Huxley Medical, Inc.