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Age and sex are two of the three major risk factors for Alzheimer’s disease (ApoE-e4 allele is the third), with women having a twofold greater risk for Alzheimer’s disease after the age of 75 years. Sex differences have been shown across a wide range of cognitive skills in young and older adults, and evidence supports a role for sex steroids, especially estradiol, in protecting against the development of cognitive decline in women. Sleep may also be a protective factor against age-related cognitive decline, since specific electrophysiological sleep events (e.g. sleep spindle/slow oscillation coupling) are critical for offline memory consolidation. Furthermore, studies in young women have shown fluctuations in sleep events and sleep-dependent memory consolidation during different phases of the menstrual cycle that are associated with the levels of sex steroids. An under-appreciated possibility is that there may be an important interaction between these two protective factors (sex steroids and sleep) that may play a role in daily fluctuations in cognitive processing, in particular memory, across a woman’s lifespan. Here, we summarize the current knowledge of sex steroid-dependent influences on sleep and cognition across the lifespan in women, with special emphasis on sleep-dependent memory processing. We further indicate gaps in knowledge that require further experimental examination in order to fully appreciate the complex and changing landscape of sex steroids and cognition. Lastly, we propose a series of testable predictions for how sex steroids impact sleep events and sleep-dependent cognition across the three major reproductive stages in women (reproductive years, menopause transition, and post-menopause).

The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users’ behaviors, including sleep. These devices are now able to capture different bio-signals for determining, for example, heart rate and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large datasets (Big Data), with the potential of offering an unprecedented window on users’ health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health, however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16–19years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9min. Proportional biases were detected for “light” and “deep” sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for “light” sleep, 59% for “deep” sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2=.16, p =.048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

The application of data-driven deep learning to identify sex differences in developing brain structures of pre-adolescents has heretofore not been accomplished. Here, the approach identifies sex differences by analyzing the minimally processed MRIs of the first 8144 participants (age 9 and 10 years) recruited by the Adolescent Brain Cognitive Development (ABCD) study. The identified pattern accounted for confounding factors (i.e., head size, age, puberty development, socioeconomic status) and comprised cerebellar (corpus medullare, lobules III, IV/V, and VI) and subcortical (pallidum, amygdala, hippocampus, parahippocampus, insula, putamen) structures. While these have been individually linked to expressing sex differences, a novel discovery was that their grouping accurately predicted the sex in individual pre-adolescents. Another novelty was relating differences specific to the cerebellum to pubertal development. Finally, we found that reducing the pattern to a single score not only accurately predicted sex but also correlated with cognitive behavior linked to working memory. The predictive power of this score and the constellation of identified brain structures provide evidence for sex differences in pre-adolescent neurodevelopment and may augment understanding of sex-specific vulnerability or resilience to psychiatric disorders and presage sex-linked learning disabilities.

Cross-sectional analyses were conducted in the baseline cohort of the Adolescent Brain and Cognitive Development (ABCD) Study to determine if lifetime low-level alcohol use was associated with an increased likelihood of lifetime suicidality (N=10,773, ages 9-10). Among the lifetime suicide ideation and attempt groups, 37.7% and 36.2% reported lifetime low-level alcohol use, respectively; versus 22.2% in the non-suicidality group. Children reporting lifetime alcohol use (i.e., ≥ a sip) showed a nearly two-fold increase in their odds of lifetime suicidality compared to those with no previous alcohol use. Future prospective research with this cohort will continue to probe alcohol-suicidality associations.

Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

OBJECTIVES: Starting in adolescence, female sex is a strong risk factor for the development of insomnia. Reasons for this are unclear but could involve altered stress reactivity and/or autonomic nervous system (ANS) dysregulation, which are strongly associated with the pathophysiology of insomnia. We investigated sex differences in the effect of stress on sleep and ANS activity in adolescents, using the first night in the laboratory as an experimental sleep-related stressor. DESIGN: Repeated measures (first night vs. a subsequent night) with age (older/younger) and sex (males/females) as between factors. SETTING: Recordings were performed at the human sleep laboratory at SRI International. PARTICIPANTS: 106 healthy adolescents (Age, mean±SD: 15.2±2.0 years; 57 boys). MEASURES: Polysomnographic sleep, nocturnal heart rate (HR) and frequency-domain spectral ANS HR variability (HRV) indices. RESULTS: Boys and girls showed a first night effect, characterized by lower sleep efficiency, lower %N1 and %N2 sleep, more wake after sleep onset and %N3 sleep, altered sleep microstructure (increased high-frequency sigma and Beta1 electroencephalographic activity), and reduced vagal activity (p

Purpose The aim of the study was to investigate associations between sleep disturbances and mental health in adolescents. Methods Data are from a national sample of 11,670 U.S. participants (5,594 females, aged 9–10 years, 63.5% white) in the Adolescent Brain Cognitive Development study. Initial longitudinal analyses were conducted for a subset of the sample (n = 4,951). Measures of youth sleep disturbance (disorders of initiating and maintaining sleep, sleep–wake transition disorders, and disorders of excessive somnolence) and “typical” total sleep time (number of hours slept on most nights in the past 6 months) were obtained from the parent-report Sleep Disturbance Scale (Data Release 2.0). Parent-report measures of youth mental health (depression, internalizing, and externalizing behaviors) from the Child Behavior Checklist and typical screen time were included. Results At baseline, greater sleep disturbance and shorter total sleep time were associated with greater internalizing, externalizing, and depression scores. After controlling for baseline mental health symptoms, baseline sleep disturbance significantly predicted depression and internalizing and externalizing scores at 1-year follow-up. A significant interaction with sex indicated that the association between disorders of excessive somnolence and depression 1 year later was steeper for girls, compared with boys (p Conclusions Sleep disturbances predicted future mental health, particularly depression in this young sample, highlighting the potential to harness sleep as a tool to mitigate the persistence of depression across early adolescence and potentially prevent an adolescent onset of major depressive disorder.

Introduction Commercial wearable devices have shown the capability of collecting and processing multisensor information (motion, cardiac activity), claiming to be able to measure sleep-wake patterns and differentiate sleep stages. While using these devices, users should be aware of their accuracy, sources of measurement error and contextual factors that may affect their performance. Here, we evaluated the agreement between Fitbit Charge 2™ and PSG in adults, considering effects of two different sleep classification methods and pre-sleep alcohol consumption. Methods Laboratory-based synchronized recordings of device and PSG data were obtained from 14 healthy adults (42.6±9.7y; 6 women), who slept between one and three nights in the lab, for a total of 27 nights of data. On 10 of these nights, participants consumed alcohol (up to 4 standard drinks) in the 2 hours before bedtime. Device performance relative to PSG was evaluated using epoch-by-epoch and Bland-Altman analyses, with device data obtained from a data-management platform, Fitabase, via two methods one that accounts for short wakes (SW, awakenings that last less than 180s) and one that does not (not-SW). Results SW and not-SW methods were similar in scoring (96.76% agreement across epochs), although the SW method had better accuracy for differentiating “light”, “deep”, and REM sleep; but produced more false positives in wake detection. The device (SW-method) classified epochs of wake, “light” (N1+N2), “deep” (N3) and REM sleep with 56%, 77%, 46%, and 62% sensitivity, respectively. Bland-Altman analysis showed that the device significantly underestimated “light” (~19min) and “deep” (~26min) sleep. Alcohol consumption enhanced PSG-device discrepancies, in particular for REM sleep (p=0.01). Conclusion Our results indicate promising accuracy in sleep-wake and sleep stage identification for this device, particularly when accounting for short wakes, as compared to PSG. Alcohol consumption, as well as other potential confounders that could affect measurement accuracy should be further investigated. Support This study was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant R21-AA024841 (IMC and MdZ). The content is solely the responsibility of the authors and does not necessarily represent the official views the National Institutes of Health.

Study Objectives To investigate the pre-sleep psychophysiological state and the arousal deactivation process across the sleep onset (SO) transition in adolescents. Methods Data were collected from a laboratory overnight recording in 102 healthy adolescents (48 girls, 12–20 years old). Measures included pre-sleep self-reported cognitive/somatic arousal, and cortical electroencephalographic (EEG) and electrocardiographic activity across the SO transition. Results Adolescent girls, compared with boys, reported higher pre-sleep cognitive activation (p = 0.025) and took longer to fall asleep (p Conclusions Our findings show the dynamics of the cortical-cardiac de-arousing process across the SO transition in a non-clinical sample of healthy adolescents. Findings suggest a female-specific vulnerability to inefficient sleep initiation, which may contribute to their greater risk for developing insomnia.

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19-61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS,15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep ("light sleep"), 0.49 accuracy in detecting N3 sleep ("deep sleep"), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland-Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM-REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).

During the course of adolescence, reductions occur in cortical thickness and gray matter (GM) volume, along with a 65% reduction in slow wave (delta) activity during sleep (SWA) but empirical data linking these structural brain and functional sleep differences, is lacking. Here we investigated specifically whether age-related differences in cortical thickness and GM volume and cortical thickness accounted for the typical age-related difference in slow wave (delta) activity (SWA) during sleep. 132 healthy participants (age: 12–21 years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were included in this cross-sectional analysis of baseline polysomnographic, electroencephalographic (EEG) and magnetic resonance imaging (MRI) data. By applying mediation models we identified a large, direct effect of age on SWA in adolescents, which explained 45% of the variance in ultra-SWA (0.3 to 1Hz) and 52% of the variance in delta-SWA (1Hz to

Hot flashes (HFs) are a hallmark of menopause in midlife women. They are beyond bothersome symptoms, having a profound impact on quality of life and wellbeing, and are a potential marker of cardiovascular (CV) disease risk. Here, we investigated the impact on CV functioning of single nocturnal HFs, considering whether or not they were accompanied by arousals or awakenings. We investigated changes in heart rate (HR, 542 HFs), blood pressure (BP, 261 HFs), and pre-ejection period (PEP, 168 HFs) across individual nocturnal physiological HF events in women in the menopausal transition or post-menopause (age: 50.7 ± 3.6 years) (n = 86 for HR, 45 for BP, 27 for PEP). HFs associated with arousals/awakenings (51.1%), were accompanied by an increase in systolic (SBP; ~6 mmHg) and diastolic (DBP; ~5 mmHg) BP and HR (~20% increase), sustained for several minutes. In contrast, HFs occurring in undisturbed sleep (28.6%) were accompanied by a drop in SBP and a marginal increase in HR, likely components of the heat dissipation response. All HFs were accompanied by decreased PEP, suggesting increased cardiac sympathetic activity, with a prolonged increase for HFs associated with sleep disruption. Older age predicted greater likelihood of HF-related sleep disturbance. HFs were less likely to wake a woman in rapid-eye-movement and slow-wave sleep. Findings show that HFs associated with sleep disruption, which are in the majority and more likely in older women, lead to increases in HR and BP, which could have long-term impact on nocturnal CV restoration in women with multiple HFs.

Importance Incidental findings (IFs) are unexpected abnormalities discovered during imaging and can range from normal anatomic variants to findings requiring urgent medical intervention. In the case of brain magnetic resonance imaging (MRI), reliable data about the prevalence and significance of IFs in the general population are limited, making it difficult to anticipate, communicate, and manage these findings. Objectives To determine the overall prevalence of IFs in brain MRI in the nonclinical pediatric population as well as the rates of specific findings and findings for which clinical referral is recommended. Design, setting, and participants This cohort study was based on the April 2019 release of baseline data from 11 810 children aged 9 to 10 years who were enrolled and completed baseline neuroimaging in the Adolescent Brain Cognitive Development (ABCD) study, the largest US population-based longitudinal observational study of brain development and child health, between September 1, 2016, and November 15, 2018. Participants were enrolled at 21 sites across the US designed to mirror the demographic characteristics of the US population. Baseline structural MRIs were centrally reviewed for IFs by board-certified neuroradiologists and findings were described and categorized (category 1, no abnormal findings; 2, no referral recommended; 3; consider referral; and 4, consider immediate referral). Children were enrolled through a broad school-based recruitment process in which all children of eligible age at selected schools were invited to participate. Exclusion criteria were severe sensory, intellectual, medical, or neurologic disorders that would preclude or interfere with study participation. During the enrollment process, demographic data were monitored to ensure that the study met targets for sex, socioeconomic, ethnic, and racial diversity. Data were analyzed from March 15, 2018, to November 20, 2020. Main outcomes and measures Percentage of children with IFs in each category and prevalence of specific IFs. Results A total of 11 679 children (52.1% boys, mean [SD] age, 9.9 [0.62] years) had interpretable baseline structural MRI results. Of these, 2464 participants (21.1%) had IFs, including 2013 children (17.2%) assigned to category 2, 431 (3.7%) assigned to category 3, and 20 (0.2%) assigned to category 4. Overall rates of IFs did not differ significantly between singleton and twin gestations or between monozygotic and dizygotic twins, but heritability analysis showed heritability for the presence or absence of IFs (h2 = 0.260; 95% CI, 0.135-0.387). Conclusions and relevance Incidental findings in brain MRI and findings with potential clinical significance are both common in the general pediatric population. By assessing IFs and concurrent developmental and health measures and following these findings over the longitudinal study course, the ABCD study has the potential to determine the significance of many common IFs.

Introduction Sleep disturbances frequently emerge during adolescence amongst profound, normative, sleep maturation and biopsychosocial changes. Factors like stress, worry or rumination may make falling asleep and maintaining sleep more difficult. Here, we evaluate the efficacy of a novel intervention based on virtual reality (VR) and slow breathing to promote bedtime relaxation and facilitate sleep in high-school adolescents. Methods Twenty-nine 16-18 year-old adolescents with (N=9, 6 girls) and without (N=20, 11 girls) sleep difficulties underwent two counterbalanced in-lab relaxation and baseline polysomnography (PSG) nights. For the relaxation condition, immediately preceding bedtime, participants were engaged in slow diaphragmatic breathing (to promote physiological downregulation) whilst passively experiencing a relaxation immersive VR environment, designed to promote cognitive relaxation/distraction (20min). On the baseline night, participants engaged in quiet activities (e.g., reading a book) before bedtime (20min). Results The VR intervention resulted in a significant immediate increase in perceived relaxation and reduced worry (p Conclusion Our data highlight the potential for combining cognitive relaxation/distraction strategies, using immersive VR technology and physiological downregulation, to promote bedtime relaxation and improve overall sleep quality in adolescents. Further research is needed to evaluate the feasibility and effectiveness of such interventions over time. Support National Heart, Lung and Blood Institute (NHLBI) R01HL139652 (to MdZ)

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.HighlightsAn overview of the MRI processing pipeline for the ABCD StudyA discussion on the challenges of large, multisite population studiesA methodological reference for users of publicly shared data from the ABCD Study

The preejection period (PEP) is a valid index of myocardial contractility and beta-adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B-point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement, are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals (Forouzanfar, et al., 2018). We investigated the algorithms’ performance relative to expert visual scoring on long-duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements, and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B-point detection algorithm in different sleep stages and in-bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters.

Objective Sleep changes substantially during adolescence; however, our understanding of age-related differences in specific electroencephalographic waveforms during this developmental period is limited. Method Sigma power, spindle characteristics and cognitive data were calculated for fast (∼13 Hz) central and slow (∼11 Hz) frontal sleep spindles for a large cross-sectional sample of adolescents (N = 134, aged 12–21 years, from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study). Results Older age (and advanced pubertal development) was associated with lower absolute sigma power and greater fast spindle density, with spindles having a shorter duration and smaller amplitude and occurring at a faster average frequency than at a younger age. Spindle characteristics were not directly associated with cognition. An indirect relationship (age * density) provided some evidence for an association between better episodic memory performance and greater spindle density only for younger adolescents. Conclusion Spindle characteristics in adolescents differed according to age, possibly reflecting underlying differences in thalamo-cortical connectivity, and may play a role in episodic memory early in adolescence. Significance Sleep spindles may serve as a marker of adolescent development, likely reflecting brain maturational status. Investigating specific spindle characteristics, in addition to sigma power, is necessary to fully characterize spindles during adolescence.

Hot flashes (HF) are intense, transient feelings of heat usually accompanied with flushed skin and sweating that are experienced by women around the time of menopause. HFs are associated with poor quality of life and increased cardiovascular risk. Automatic detection of HF occurrence and precise timing of HF onset could provide unique insight into the physiology of the HF and its effect on the cardiovascular system. A novel automatic algorithm is proposed for the detection of HFs occurrence and timing from the sternal skin conductance signal that is robust to noise and artifacts. The method is based on the gold standard rule (2 μS rise in skin conductance within 30 s) and considers several conditions based on the skin conductance level and its derivative to reject unwanted events. ECG-derived heart rate pattern variations are studied prior to the detected HF onset. The algorithm is validated against expert detected HFs over 200 hours of sleep data collected from 12 perimenopausal women. It achieved a total accuracy of 93% and a total error of 3% in HF detection. It was observed that heart rate increased before the onset of 80% of the HFs occurring in undisturbed sleep. Application of this algorithm along with fusion of other simultaneously recorded physiological measures has the potential to advance understanding of the HF.

Introduction Prior sleep behavior has been shown to correlate with waking resting‐state functional connectivity (FC) in the default mode network (DMN). However, the impact of sleep history on FC during sleep has not been investigated. The aim of this study was to establish whether there is an association between intersubject variability in habitual sleep behaviors and the strength of FC within the regions of the DMN during non‐rapid eye movement (NREM) sleep. Methods Wrist actigraphy and sleep questionnaires were used as objective and subjective measures of habitual sleep behavior, and EEG‐functional MRI during NREM sleep was used to quantify sleep. Results There was a significant, regionally specific association between the interindividual variability in objective (total sleep time on the night before scanning) and subjective (Insomnia Severity Index) measures of prior sleep–wake behavior and the strength of DMN FC during subsequent wakefulness and NREM sleep. In several cases, FC was related to sleep measures independently of sleep stage, suggesting that previous sleep history effects sleep FC globally across the stages. Conclusions This work highlights the need to consider a subject's prior sleep history in studies utilizing FC analysis during wakefulness and sleep, and indicates the complexity of the impact of sleep on the brain both in the short and long term.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9–10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Study objectives We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN). Methods Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires (N = 33 healthy adults, mean age 34.3, standard deviation ± 11.6 y). Data were statistically analyzed using multiple linear regression. Fourteen consecutive days of wrist actigraphy in participant's home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age). Results cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC, with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST. Conclusions This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of intrinsically connected networks (ICNs) in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and inter-network functional connectivity of ICNs, an observation that may underlie the link between sleep status and cognitive performance.

Introduction Despite the thalamus’ dense connectivity with both cortical and subcortical structures, few studies have specifically investigated how thalamic connectivity changes with age and how such changes are associated with behavior. This study investigated the effect of age on thalamo‐cortical and thalamo‐hippocampal functional connectivity (FC) and the association between thalamic FC and visual–spatial memory and reaction time (RT) performance in older adults. Methods Resting‐state functional magnetic resonance images were obtained from younger (n = 20) and older (n = 20) adults. A seed‐based approach was used to assess the FC between the thalamus and (1) sensory resting‐state networks; (2) the hippocampus. Participants also completed visual–spatial memory and RT tasks, from the Cambridge Neuropsychological Test Automated Battery (CANTAB). Results Older adults exhibited a loss of specificity in the FC between sensory thalamic subregions and corresponding sensory cortex. Greater thalamo‐motor FC in older adults was associated with faster RTs. Furthermore, older adults exhibited greater thalamo‐hippocampal FC compared to younger adults, which was greatest for those with the poorest visual–spatial memory performance. Conclusion Although older adults exhibited poorer visual–spatial memory and slower reaction times compared to younger adults, “good” and “poorer” older performers exhibited different patterns of thalamo‐cortical and thalamo‐hippocampal FC. These results highlight the potential role of thalamic connectivity in supporting reaction times and memory in aging. Furthermore, these results highlight the importance of including the thalamus in studies of aging to fully understand how brain changes with age may be associated with behavior.

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Conventional functional connectivity (FC) analysis of fMRI data derives a single measurement from the entire scan, generally several minutes in duration, which neglects the brain's dynamic behaviour and potentially loses important temporal information. Short-interval dynamic FC is an attractive proposition if methodological issues can be resolved and the approach validated. This was addressed in two ways; firstly we assessed FC of the posterior cingulate cortex (PCC) node of the default mode network (DMN) using differing temporal intervals (8 s to 5 min) in the waking-resting state. We found that 30-second intervals and longer produce spatially similar correlation topography compared to 15-minute static FC measurements, while providing increased temporal information about changes in FC that were consistent across interval lengths. Secondly, we used NREM sleep as a behavioural validation for the use of 30-second temporal intervals due to the known fMRI FC changes with sleep stage that have been observed in previous studies using intervals of several minutes. We found significant decreases in DMN FC with sleep depth which were most pronounced during stage N2 and N3. Additionally, both the proportion of time with strong PCC-DMN connectivity and the variability in dynamic FC decreased with sleep. We therefore show that dynamic FC with epochs as short as tens of seconds is a viable method for characterising intrinsic brain activity.

Highlights • Exposure to temporal sequences improves prediction of future events. • Learning to predict from temporal sequences generalizes to untrained stimuli. • Learning to predict is sensitive to the global structure of the trained sequence. • Learning to predict is compromised by increased attentional load., Previous experience is thought to facilitate our ability to extract spatial and temporal regularities from cluttered scenes. However, little is known about how we may use this knowledge to predict future events. Here we test whether exposure to temporal sequences facilitates the visual recognition of upcoming stimuli. We presented observers with a sequence of leftwards and rightwards oriented gratings that was interrupted by a test stimulus. Observers were asked to indicate whether the orientation of the test stimulus matched their expectation based on the preceding sequence. Our results demonstrate that exposure to temporal sequences without feedback facilitates our ability to predict an upcoming stimulus. In particular, observers’ performance improved following exposure to structured but not random sequences. Improved performance lasted for a prolonged period and generalized to untrained stimulus orientations rather than sequences of different global structure, suggesting that observers acquire knowledge of the sequence structure rather than its items. Further, this learning was compromised when observers performed a dual task resulting in increased attentional load. These findings suggest that exposure to temporal regularities in a scene allows us to accumulate knowledge about its global structure and predict future events.