Improved implicit self-esteem is associated with extended antidepressant effects following a novel synergistic intervention.

INTRODUCTION: In a previously published randomized controlled trial, automated self-association training (ASAT), a novel digital intervention, was found to extend the rapid antidepressant effect of a single infusion of ketamine for at least 30 days. In this secondary analysis, we aimed to understand the potential role of implicit self-esteem in the combined antidepressant effect of ketamine and ASAT training, by investigating the novel synergistic treatment's effects on implicit self-associations and their relation to symptom improvement. METHODS: A total of 154 adults (ages 18-60) with treatment-resistant unipolar depression and lower-than-normative explicit self-esteem were randomized in a double-blind, parallel-arm design to receive one of three treatment allocations: an active/active treatment combination consisting of one infusion of ketamine (0.5 mg/kg) followed by four days of ASAT ( ~ 30-40 min/day), or one of two control arms that lacked either the active drug or the active behavioral component. The Implicit Association Test (IAT) was used to behaviorally assess the strength of association between self-related stimuli and negative concepts. Linear regression models were used to test the relationship between group assignment, IAT scores acquired immediately post-treatment, and both acute and extended clinical outcomes (% change in Montgomery-Asberg Depression Rating Scale scores, relative to pre-treatment baseline) in the trial. RESULTS: The group assigned to ketamine + ASAT intervention, compared to the other groups, had a pattern of IAT scores indicating more positive self-associations immediately after treatment relative to the control arms (F(1, 131) = 3.979; p = 0.048). In regression models, IAT scores tracked with concurrent (acute post-treatment) % change in MADRS scores across all treatment arms (p = 0.001), and mediated more extended (Day 30) depression improvements specifically for the ketamine+ASAT arm (group * IAT interaction term: ß = -0.201; p = 0.049). DISCUSSION: Our findings suggest that changing implicit self-worth during a post-ketamine 'plasticity window' is one key mechanism whereby the novel ketamine+ASAT treatment combination exerts its antidepressant benefit, confirming the intended treatment target at the level of implicit cognition. Future studies should seek to further enhance the reliability of the biobehavioral intervention's impact on implicit cognition, as this mechanism appears linked to the intervention's enduring clinical benefits.

Pioneering a Multi-Phase Framework to Harmonize Self-Reported Sleep Data Across Cohorts.

STUDY OBJECTIVES: Harmonizing and aggregating data across studies enable pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data. METHODS: We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items; (2) group items into domains; (3) harmonize items; and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five United States cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation. RESULTS: We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of Satisfaction, Alertness/Sleepiness, Timing, Efficiency, Duration, Insomnia, and Sleep Apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g., timing, total sleep time, efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g., wake-up time, duration) and more heterogeneous (e.g., time in bed, bedtime) across samples. CONCLUSIONS: Our process can guide researchers and cohort stewards towards effective sleep harmonization and provides a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.

Ten-Year Stability of an Insomnia Sleeper Phenotype and Its Association With Chronic Conditions.

OBJECTIVE: To identify distinct sleep health phenotypes in adults, examine transitions in sleep health phenotypes over time, and subsequently relate these to the risk of chronic conditions. METHODS: A national sample of adults from the Midlife in the United States study ( N = 3683) provided longitudinal data with two time points (T1: 2004-2006, T2: 2013-2017). Participants self-reported on sleep health (regularity, satisfaction, alertness, efficiency, duration) and the number and type of chronic conditions. Covariates included age, sex, race, education, education, partnered status, number of children, work status, smoking, alcohol, and physical activity. RESULTS: Latent transition analysis identified four sleep health phenotypes across both time points: good sleepers, insomnia sleepers, weekend catch-up sleepers, and nappers. Between T1 and T2, the majority (77%) maintained their phenotype, with the nappers and insomnia sleepers being the most stable. In fully adjusted models with good sleepers at both time points as the reference, being an insomnia sleeper at either time point was related to having an increased number of total chronic conditions by 28%-81% at T2, adjusting for T1 conditions. Insomnia sleepers at both time points were at 72%-188% higher risk for cardiovascular disease, diabetes, depression, and frailty. Being a napper at any time point related to increased risks for diabetes, cancer, and frailty. Being a weekend catch-up sleeper was not associated with chronic conditions. Those with lower education and unemployed were more likely to be insomnia sleepers; older adults and retirees were more likely to be nappers. CONCLUSION: Findings indicate a heightened risk of chronic conditions involved in suboptimal sleep health phenotypes, mainly insomnia sleepers.

Effect of daily discrimination on naturalistic sleep health features in young adults.

OBJECTIVE: Racial inequities in sleep health are well documented and may be partially attributable to discrimination experiences. However, the effects of acute discrimination experiences on same-night sleep health are understudied. We quantified naturalistic discrimination experiences captured using ecological momentary assessment (EMA) and examined whether reporting discrimination on a given day predicted sleep health that night. METHOD: Participants completed baseline assessments and a 17-day EMA protocol, with text prompts delivered four times daily to collect discrimination experiences. Seven different daily sleep characteristics were ascertained each morning. Discrimination reasons (e.g., because of my racial identity) were reported by participants and categorized into any, racial, or nonracial discrimination. Outcomes included the seven sleep diary characteristics. We fit generalized linear mixed effects models for each sleep outcome and discrimination category, controlling for key covariates. RESULTS: The analytic sample included 116 self-identified Black and White individuals (48% Black, 71% assigned female at birth, average age = 24.5 years). Among Black participants, race-based discrimination was associated with a 0.5-hr reduction in total sleep time (TST). Among White individuals, nonracial discrimination was associated with a 0.6-hr reduction in TST, an earlier sleep offset, and reduced sleep efficiency (partly attributable to more nighttime awakenings). CONCLUSIONS: Young adults may sleep worse on nights after experiencing discrimination, and different types of discrimination affect different sleep outcomes for Black and White individuals. Future studies may consider developing treatments that account for different sleep vulnerabilities for people experiencing discrimination on a given day. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pre-pandemic circadian phase predicts pandemic alcohol use among adolescents.

Later circadian timing during adolescence is linked to worse sleep, more severe depression and greater alcohol involvement, perhaps due to circadian misalignment imposed by early school schedules. School schedules shifted later during the COVID-19 pandemic, ostensibly reducing circadian misalignment and potentially mitigating problems with depression and alcohol. We used the pandemic as a natural experiment to test whether adolescent drinkers with later circadian timing showed improvements in sleep, depression and alcohol involvement. Participants were 42 adolescents reporting alcohol use. We assessed circadian phase via dim light melatonin onset prior to the pandemic, then conducted remote assessments of sleep, depressive symptoms and alcohol use during the pandemic. Mixed-effects models were used to test for pandemic effects, covarying for age, sex, time since baseline evaluation, and current school/work status. Adolescents with later circadian timing reported less sleep than other teens on school nights, both before and during the pandemic. Although school night sleep increased during the pandemic (F = 28.36, p < 0.001), those increases were not greater for individuals with later circadian timing. Individuals with later circadian timing reported larger increases in alcohol use than other teens during the pandemic (X2 = 36.03, p < 0.001). Depressive symptoms increased during the pandemic (X2 = 46.51, p < 0.001) but did not differ based on circadian timing. Consistent with prior reports, adolescents with later circadian timing obtained less sleep, and later school schedules facilitated increased sleep duration. Nonetheless, individuals with later circadian timing reported the sharpest increases in alcohol use, suggesting that circadian timing contributes to risk for alcohol use beyond the effects of insufficient sleep.

Multidimensional Sleep Health Problems Across Middle and Older Adulthood Predict Early Mortality.

BACKGROUND: Having multiple sleep problems is common in adulthood. Yet, most studies have assessed single sleep variables at one timepoint, potentially misinterpreting health consequences of co-occurring sleep problems that may change over time. We investigated the relationship between multidimensional sleep health across adulthood and mortality. METHODS: Participants from the Midlife in the United States Study reported sleep characteristics in 2004-2006 (MIDUS-2; M2) and in 2013-2014 (MIDUS-3; M3). We calculated a composite score of sleep health problems across 5 dimensions: Regularity, Satisfaction, Alertness, Efficiency, and Duration (higher = more problems). Two separate models for baseline sleep health (n = 5 140; median follow-up time = 15.3 years) and change in sleep health (n = 2 991; median follow-up time = 6.4 years) to mortality were conducted. Cox regression models controlled for sociodemographics and key health risk factors (body mass index, smoking, depressive symptoms, diabetes, and hypertension). RESULTS: On average, 88% of the sample reported having one or more sleep health problems at M2. Each additional sleep health problem at M2 was associated with 12% greater risk of all-cause mortality (hazard ratio [HR] = 1.12, 95% confidence interval [CI] = 1.04-1.21), but not heart disease-related mortality (HR = 1.14, 95% CI = 0.99-1.31). An increase in sleep health problems from M2 to M3 was associated with 27% greater risk of all-cause mortality (HR = 1.27, 95% CI = 1.005-1.59), and 153% greater risk of heart disease mortality (HR = 2.53, 95% CI = 1.37-4.68). CONCLUSIONS: More sleep health problems may increase the risk of early mortality. Sleep health in middle and older adulthood is a vital sign that can be assessed at medical checkups to identify those at greater risk.

Sleep Disorders and Cognitive Aging Among Cognitively Impaired Versus Unimpaired Older Adults.

BACKGROUND AND OBJECTIVES: Sleep disorders often predict or co-occur with cognitive decline. Yet, little is known about how the relationship unfolds among older adults at risk for cognitive decline. To examine the associations of sleep disorders with cognitive decline in older adults with unimpaired cognition or impaired cognition (mild cognitive impairment and dementia). RESEARCH DESIGN AND METHODS: A total of 5,822 participants (Mage = 70) of the National Alzheimer's Coordinating Center database with unimpaired or impaired cognition were followed for 3 subsequent waves. Four types of clinician-diagnosed sleep disorders were reported: sleep apnea, hyposomnia/insomnia, REM sleep behavior disorder, or "other." Cognition over time was measured by the Montreal Cognitive Assessment (MoCA) or an estimate of general cognitive ability (GCA) derived from scores based on 12 neuropsychological tests. Growth curve models were estimated adjusting for covariates. RESULTS: In participants with impaired cognition, baseline sleep apnea was related to better baseline MoCA performance (b = 0.65, 95% confidence interval [95% CI] = [0.07, 1.23]) and less decline in GCA over time (b = 0.06, 95% CI = [0.001, 0.12]). Baseline insomnia was related to better baseline MoCA (b = 1.54, 95% CI = [0.88, 2.21]) and less decline in MoCA over time (b = 0.56, 95% CI = [0.20, 0.92]). Furthermore, having more sleep disorders (across the 4 types) at baseline predicted better baseline MoCA and GCA, and less decline in MoCA and GCA over time. These results were only found in those with impaired cognition and generally consistent when using self-reported symptoms of sleep apnea or insomnia. DISCUSSION AND IMPLICATIONS: Participants with sleep disorder diagnoses may have better access to healthcare, which may help maintain cognition through improved sleep.

Digital CBTI hubs as a treatment augmentation strategy in military clinics: study protocol for a pragmatic randomized clinical trial.

BACKGROUND: Chronic insomnia is the most prevalent sleep disorder among military service members, and it compromises readiness, performance, and physical and mental health. Cognitive behavioral treatment for insomnia (CBTI) is the standard of care for the treatment of insomnia recommended by the VA/DoD Clinical Practice Guideline, the American Academy of Sleep Medicine, and the American College of Physicians. CBTI is highly effective but has limited scalability. It is often unavailable in clinical settings where service members receive sleep care. Digital technologies offer unique opportunities to scale and broaden the geographic reach of CBTI services and support increased patient access and engagement in behavioral sleep care. This study aims to evaluate the impact and acceptability of digital CBTI hubs to augment military treatment facilities' capabilities in behavioral sleep medicine. METHODS: This is a multi-site, non-inferiority randomized clinical trial designed to compare the effects of in-person (face-to-face or virtual) insomnia care as usual at three military sleep clinics versus CBTI delivered remotely and asynchronously through digital CBTI hubs. Digital CBTI hubs are led by licensed, certified clinicians who use NOCTEM's® evidence-based clinical decision support platform COAST™ (Clinician Operated Assistive Sleep Technology). Changes in insomnia severity and daytime symptoms of depression and anxiety will be compared at baseline, at 6-8 weeks, and at 3-month follow-up. Patient satisfaction with insomnia care as usual versus digital CBTI hubs will also be examined. We hypothesize that digital CBTI hubs will be non-inferior to insomnia care as usual for improvements in insomnia and daytime symptoms as well as patient satisfaction with insomnia care. DISCUSSION: Digital technology has a high potential to scale CBTI accessibility and delivery options required to meet the insomnia care needs of military service members. Digital CBTI hubs using COAST offer a novel approach to broaden service members' access to CBTI and to serve as an augmentation strategy for existing sleep services at military treatment facilities. The pragmatic approach leveraging technology in this trial has the potential to rapidly inform clinical practice within the Defense Health Agency as well as other healthcare systems. TRIAL REGISTRATION: ClinicalTrials.gov NCT05490550. Registered on 14 July 2023.

Race and sex differences in the longitudinal changes in multidimensional self-reported sleep health characteristics in aging older adults.

OBJECTIVES: We examined within-individual changes in self-reported sleep health as community-dwelling older adults age as well as potential differences in these changes by self-reported sex and racial identity. METHODS: Participants were from the United States and enrolled in the Rush Memory and Aging Project, Minority Aging Research Study, or Religious Orders Study (N = 3539, 20% Black, 75% female, mean 78years [range 65-103]), and they received annual, in-person clinical evaluations (median 5 visits [range 1-27]). A sleep health composite score measured the number of poor sleep characteristics among satisfaction, daytime sleepiness, efficiency, and duration. Mixed effects models estimated associations of age, race, sex, and their interactions on the composite and individual sleep measures, accounting for key confounders. RESULTS: As they aged, Black participants shifted from reporting two poor sleep characteristics to one poor sleep characteristic, while White participants shifted from one poor characteristic to two. Regardless of age, sex, and race, participants reported that they "often" felt satisfied with their sleep and "sometimes" had trouble staying asleep. Females over age 85 and males of all ages reported the most daytime sleepiness, and older White participants (>age 90) reported the most difficulty falling asleep. CONCLUSIONS: Although self-reported sleep characteristics were typically stable across age, identifying race and sex differences in self-reported sleep health can help guide future research to understand the mechanisms that underlie these differences.

Examining demographic and psychosocial factors related to self-weighing behavior during pregnancy and postpartum periods.

Black childbearing individuals in the US experience a higher risk of postpartum weight retention (PPWR) compared to their White counterparts. Given that PPWR is related to adverse health outcomes, it is important to investigate predictors of weight-related health behaviors, such as self-weighing (i.e., using a scale at home). Regular self-weighing is an evidence-based weight management strategy, but there is minimal insight into sociodemographic factors related to frequency. The Postpartum Mothers Mobile Study (PMOMS) facilitated longitudinal ambulatory weight assessments to investigate racial inequities in PPWR. Our objective for the present study was to describe self-weighing behavior during and after pregnancy in the PMOMS cohort, as well as related demographic and psychosocial factors. Applying tree modeling and multiple regression, we examined self-weighing during and after pregnancy. Participants (N = 236) were 30.2 years old on average (SD = 4.7), with the majority being college-educated (53.8%, n = 127), earning at least $30,000 annually (61.4%, n = 145), and self-identifying as non-Hispanic White (NHW; 68.2%, n = 161). Adherence to regular self-weighing (at least once weekly) was highest among participants during pregnancy, with a considerable decline after giving birth. Low-income Black participants (earning < $30,000) were significantly less likely to reach a completion rate of ≥ 80% during pregnancy (AOR = 0.10) or the postpartum period (AOR = 0.16), compared to NHW participants earning at least $30,000 annually. Increases in perceived stress were associated with decreased odds of sustained self-weighing after delivery (AOR = 0.79). Future research should consider behavioral differences across demographic intersections, such as race and socioeconomic status, and the impact on efficacy of self-weighing.

Prototyping Apps for the Management of Sleep, Fatigue, and Behavioral Health in Austere Far-Forward Environments: Development Study.

BACKGROUND: Military service inherently includes frequent periods of high-stress training, operational tempo, and sustained deployments to austere far-forward environments. These occupational requirements can contribute to acute and chronic sleep disruption, fatigue, and behavioral health challenges related to acute and chronic stress and disruption of team dynamics. To date, there is no centralized mobile health platform that supports self- and supervised detection, monitoring, and management of sleep and behavioral health issues in garrison and during and after deployments. OBJECTIVE: The objective of this study was to adapt a clinical decision support platform for use outside clinical settings, in garrison, and during field exercises by medics and soldiers to monitor and manage sleep and behavioral health in operational settings. METHODS: To adapt an existing clinical decision support digital health platform, we first gathered system, content, and context-related requirements for a sleep and behavioral health management system from experts. Sleep and behavioral health assessments were then adapted for prospective digital data capture. Evidence-based and operationally relevant educational and interventional modules were formatted for digital delivery. These modules addressed the management and mitigation of sleep, circadian challenges, fatigue, stress responses, and team communication. Connectivity protocols were adapted to accommodate the absence of cellular or Wi-Fi access in deployed settings. The resulting apps were then tested in garrison and during 2 separate field exercises. RESULTS: Based on identified requirements, 2 Android smartphone apps were adapted for self-monitoring and management for soldiers (Soldier app) and team supervision and intervention by medics (Medic app). A total of 246 soldiers, including 28 medics, received training on how to use the apps. Both apps function as expected under conditions of limited connectivity during field exercises. Areas for future technology enhancement were also identified. CONCLUSIONS: We demonstrated the feasibility of adapting a clinical decision support platform into Android smartphone-based apps to collect, save, and synthesize sleep and behavioral health data, as well as share data using adaptive data transfer protocols when Wi-Fi or cellular data are unavailable. The AIRE (Autonomous Connectivity Independent System for Remote Environments) prototype offers a novel self-management and supervised tool to augment capabilities for prospective monitoring, detection, and intervention for emerging sleep, fatigue, and behavioral health issues that are common in military and nonmilitary high-tempo occupations (eg, submarines, long-haul flights, space stations, and oil rigs) where medical expertise is limited.

The association between multidimensional sleep health and gestational weight gain.

BACKGROUND: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep health framework is needed. OBJECTIVES: This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG). METHODS: Sleep domains (i.e. regularity, nap duration, timing, efficiency and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined 'healthy' sleep in each domain with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD) and high (>+1 SD). RESULTS: Nearly 50% of the participants had a healthy sleep profile (i.e. healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of unhealthy sleep in each domain. The individual sleep domains were associated with a 20%-30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null. CONCLUSIONS: Future research should determine whether sleep health is an intervention target for healthy GWG.

Multi-dimensional sleep and mortality: The Multi-Ethnic Study of Atherosclerosis.

STUDY OBJECTIVES: Multiple sleep characteristics are informative of health, sleep characteristics cluster, and sleep health can be described as a composite of positive sleep attributes. We assessed the association between a sleep score reflecting multiple sleep dimensions, and mortality. We tested the hypothesis that more favorable sleep (higher sleep scores) is associated with lower mortality. METHODS: The Multi-Ethnic Study of Atherosclerosis (MESA) is a racially and ethnically-diverse multi-site, prospective cohort study of US adults. Sleep was measured using unattended polysomnography, 7-day wrist actigraphy, and validated questionnaires (2010-2013). 1726 participants were followed for a median of 6.9 years (Q1-Q3, 6.4-7.4 years) until death (171 deaths) or last contact. Survival models were used to estimate the association between the exposure of sleep scores and the outcome of all-cause mortality, adjusting for socio-demographics, lifestyle, and medical comorbidities; follow-up analyses examined associations between individual metrics and mortality. The exposure, a sleep score, was constructed by an empirically-based Principal Components Analysis on 13 sleep metrics, selected a priori. RESULTS: After adjusting for multiple confounders, a 1 standard deviation (sd) higher sleep score was associated with 25% lower hazard of mortality (Hazard Ratio [HR]: 0.75; 95% Confidence interval: [0.65, 0.87]). The largest drivers of this association were: night-to-night sleep regularity, total sleep time, and the Apnea-Hypopnea Index. CONCLUSION: More favorable sleep across multiple characteristics, operationalized by a sleep score, is associated with lower risk of death in a diverse US cohort of adults. Results suggest that interventions that address multiple dimensions may provide novel approaches for improving health.

Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction.

BACKGROUND: Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine. Unfortunately, when it comes to understanding why machine learning models are predictive, applied research continues to rely on 'out of bag' (OOB) variable importance metrics (VIMPs) that are known to have considerable shortcomings within the statistics community. After explaining the limitations of OOB VIMPs - including bias towards correlated features and limited interpretability - we describe a modern approach called 'knockoff VIMPs' and explain its advantages. METHODS: We first evaluate current VIMP practices through an in-depth literature review of 50 recent random forest manuscripts. Next, we recommend organized and interpretable strategies for analysis with knockoff VIMPs, including computing them for groups of features and considering multiple model performance metrics. To demonstrate methods, we develop a random forest to predict 5-year incident stroke in the Sleep Heart Health Study and compare results based on OOB and knockoff VIMPs. RESULTS: Nearly all papers in the literature review contained substantial limitations in their use of VIMPs. In our demonstration, using OOB VIMPs for individual variables suggested two highly correlated lung function variables (forced expiratory volume, forced vital capacity) as the best predictors of incident stroke, followed by age and height. Using an organized analytic approach that considered knockoff VIMPs of both groups of features and individual features, the largest contributions to model sensitivity were medications (especially cardiovascular) and measured medical risk factors, while the largest contributions to model specificity were age, diastolic blood pressure, self-reported medical risk factors, polysomnography features, and pack-years of smoking. Thus, we reach very different conclusions about stroke risk factors using OOB VIMPs versus knockoff VIMPs. CONCLUSIONS: The near-ubiquitous reliance on OOB VIMPs may provide misleading results for researchers who use such methods to guide their research. Given the rapid pace of scientific inquiry using machine learning, it is essential to bring modern knockoff VIMPs that are interpretable and unbiased into widespread applied practice to steer researchers using random forest machine learning toward more meaningful results.

Rapid neuroplasticity changes and response to intravenous ketamine: a randomized controlled trial in treatment-resistant depression.

Intravenous ketamine is posited to rapidly reverse depression by rapidly enhancing neuroplasticity. In human patients, we quantified gray matter microstructural changes on a rapid (24-h) timescale within key regions where neuroplasticity enhancements post-ketamine have been implicated in animal models. In this study, 98 unipolar depressed adults who failed at least one antidepressant medication were randomized 2:1 to a single infusion of intravenous ketamine (0.5 mg/kg) or vehicle (saline) and completed diffusion tensor imaging (DTI) assessments at pre-infusion baseline and 24-h post-infusion. DTI mean diffusivity (DTI-MD), a putative marker of microstructural neuroplasticity in gray matter, was calculated for 7 regions of interest (left and right BA10, amygdala, and hippocampus; and ventral Anterior Cingulate Cortex) and compared to clinical response measured with the Montgomery-Asberg Depression Rating Scale (MADRS) and the Quick Inventory of Depressive Symptoms-Self-Report (QIDS-SR). Individual differences in DTI-MD change (greater decrease from baseline to 24-h post-infusion, indicative of more neuroplasticity enhancement) were associated with larger improvements in depression scores across several regions. In the left BA10 and left amygdala, these relationships were driven primarily by the ketamine group (group * DTI-MD interaction effects: p = 0.016-0.082). In the right BA10, these associations generalized to both infusion arms (p = 0.007). In the left and right hippocampus, on the MADRS only, interaction effects were observed in the opposite direction, such that DTI-MD change was inversely associated with depression change in the ketamine arm specifically (group * DTI-MD interaction effects: p = 0.032-0.06). The acute effects of ketamine on depression may be mediated, in part, by acute changes in neuroplasticity quantifiable with DTI.

One-Year Outcomes Following Intravenous Ketamine Plus Digital Training Among Patients with Treatment-Resistant Depression: A Secondary Analysis of a Randomized Clinical Trial.

This secondary analysis of a randomized clinical trial examines whether automated self-association training can prolong the antidepressant effect of a single infusion of ketamine beyond 1 month in patients with treatment-resistant depression.

Neurobehavioral Reward and Sleep-Circadian Profiles Predict Present and Next-Year Mania/Hypomania Symptoms.

BACKGROUND: Heightened reward sensitivity/impulsivity, related neural activity, and sleep-circadian disruption are important risk factors for bipolar spectrum disorders, the defining feature of which is mania/hypomania. Our goal was to identify neurobehavioral profiles based on reward and sleep-circadian features and examine their specificity to mania/hypomania versus depression vulnerability. METHODS: At baseline, a transdiagnostic sample of 324 adults (18-25 years) completed trait measures of reward sensitivity (Behavioral Activation Scale), impulsivity (UPPS-P-Negative Urgency), and a functional magnetic resonance imaging card-guessing reward task (left ventrolateral prefrontal activity to reward expectancy, a neural correlate of reward motivation and impulsivity, was extracted). At baseline, 6-month follow-up, and 12-month follow-up, the Mood Spectrum Self-Report Measure - Lifetime Version assessed lifetime predisposition to subthreshold-syndromal mania/hypomania, depression, and sleep-circadian disturbances (insomnia, sleepiness, reduced sleep need, rhythm disruption). Mixture models derived profiles from baseline reward, impulsivity, and sleep-circadian variables. RESULTS: Three profiles were identified: 1) healthy (no reward or sleep-circadian disruption; n = 162); 2) moderate-risk (moderate reward and sleep-circadian disruption; n = 109); and 3) high-risk (high impulsivity and sleep-circadian disruption; n = 53). At baseline, the high-risk group had significantly higher mania/hypomania scores than the other groups but did not differ from the moderate-risk group in depression scores. Over the follow-up period, the high-risk and moderate-risk groups exhibited elevated mania/hypomania scores, whereas depression scores increased at a faster rate in the healthy group than in the other groups. CONCLUSIONS: Cross-sectional and next-year predisposition to mania/hypomania is associated with a combination of heightened reward sensitivity and impulsivity, related reward circuitry activity, and sleep-circadian disturbances. These measures can be used to detect mania/hypomania risk and provide targets to guide and monitor interventions.

Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study.

OBJECTIVES: The concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor. METHODS: Wrist actigraphy measures of participants (N = 289, Mage = 77.2 years, 67% females; 47% White, 40% Black, 13% Hispanic/Others) over 2 weeks were used in exploratory factor analysis to determine factor structures, followed by confirmatory factor analysis on a different subsample. The utility of this approach was demonstrated by associations with global cognitive performance (Montreal Cognitive Assessment). RESULTS: Exploratory factor analysis identified six factors: Regularity: standard deviations of four sleep measures: midpoint, sleep onset time, night total sleep time (TST), and 24-hour TST; Alertness/Sleepiness (daytime): amplitude, napping (mins and #/day); Timing: sleep onset, midpoint, wake-time (of nighttime sleep); up-mesor, acrophase, down-mesor; Efficiency: sleep maintenance efficiency, wake after sleep onset; Duration: night rest interval(s), night TST, 24-hour rest interval(s), 24-hour TST; Rhythmicity (pattern across days): mesor, alpha, and minimum. Greater sleep efficiency was associated with better Montreal Cognitive Assessment performance (ß [95% confidence interval] = 0.63 [0.19, 1.08]). CONCLUSIONS: Actigraphic records over 2 weeks revealed that Rhythmicity may be an independent factor in sleep health. Facets of sleep health can facilitate dimension reduction, be considered predictors of health outcomes, and be potential targets for sleep interventions.

Corrigendum: Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment.

[This corrects the article DOI: 10.3389/fdgth.2022.870522.].