Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression.

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.

Empirical clustering to identify individuals for whom insomnia is more closely related to suicidal ideation.

BACKGROUND: Although the effect sizes are modest, insomnia is consistently associated with suicidal thoughts and behaviors. Subgroup analyses can efficiently identify for whom insomnia is most relevant to suicidal ideation. To improve clinical case identification, the present study sought to identify subclusters of lifetime suicidal ideators for whom insomnia was most closely related to current suicidal ideation. METHODS: Data on N = 4750 lifetime suicidal ideators were extracted from the Military Suicide Research Consortium's Common Data Elements. Data on sociodemographic characteristics, severity and history of suicidal thoughts and behaviors, and related clinical characteristics were clustered by unsupervised machine learning algorithms. Robust Poisson regression estimated cluster by insomnia associations with current suicidal ideation. RESULTS: Three clusters were identified: a modest symptom severity cluster (N = 1757, 37.0 %), an elevated severity cluster (N = 1444 30.4 %), and a high severity cluster (N = 1549 32.6 %). In Cluster 1, insomnia was associated with current suicidal ideation (PRR 1.29 [1.13-1.46]) and remained significant after adjusting for sociodemographic and clinical covariates. In Cluster 2, insomnia was associated with current suicidal ideation (PRR 1.14 [1.01-1.30]), but not after adjusting for sociodemographic and clinical covariates. In Cluster 3, insomnia was associated with current suicidal ideation (PRR 1.12 [1.03-1.21]) and remained significant after adjusting for sociodemographic covariates, but not clinical covariates. LIMITATIONS: Cross-sectional design, lack of diagnostic data, non-representative sample. CONCLUSION: Insomnia appears more closely related to current suicidal ideation among modest severity individuals than other subgroups. Future work should use prospective designs and more comprehensive risk factor measures to confirm these findings.

Psychological Resilience and Hardiness as Protective Factors in the Relationship Between Depression/Anxiety and Well-Being: Exploratory and Confirmatory Evidence.

Previous research shows depression and anxiety are negatively correlated with subjective well-being. Additionally, there is evidence psychological resilience positively influences well-being. The present study explored whether the relationship between depression/anxiety and subjective well-being might also be moderated by aspects of psychological resilience - such that depression and anxiety do not reduce well-being to the same extent in individuals high in psychological resilience traits. Participants from an exploratory sample (N = 236, Mage = 23.49) and confirmatory sample (N = 196, Mage = 24.99) completed self-report measures of depression, anxiety, well-being, resilience, and hardiness (i.e., CDRISC and DRS-15). As expected, results showed strong negative correlations between anxiety/depression and both well-being and resilience/hardiness, as well as positive correlations between well-being and resilience/hardiness. A significant interaction was also present between both resilience/hardiness and depression/anxiety in predicting well-being in the first sample. Results partially replicated in the confirmatory sample (i.e., for hardiness but not resilience). These findings add to prior work by highlighting hardiness (as measured by the DRS-15), one aspect of psychological resilience, as an important protective factor in mental health. Namely, results suggest individuals with symptoms of affective disorders may remain capable of living subjectively fulfilling lives if they possess traits of psychological resilience such as hardiness.

Individualised prediction of resilience and vulnerability to sleep loss using EEG features.

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects into sleep-loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow-wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not-resilient and vulnerable versus not-vulnerable) to predict the probability of sleep-loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not-resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not-vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep-loss phenotype.

Transcranial Magnetic Stimulation of the Default Mode Network to Improve Sleep in Individuals With Insomnia Symptoms: Protocol for a Double-Blind Randomized Controlled Trial.

BACKGROUND: Cortical hyperarousal and ruminative thinking are common aspects of insomnia that have been linked with greater connectivity in the default mode network (DMN). Therefore, disrupting network activity within the DMN may reduce cortical and cognitive hyperarousal and facilitate better sleep. OBJECTIVE: This trial aims to establish a novel, noninvasive method for treating insomnia through disruption of the DMN with repetitive transcranial magnetic stimulation, specifically with continuous theta burst stimulation (cTBS). This double-blind, pilot randomized controlled trial will assess the efficacy of repetitive transcranial magnetic stimulation as a novel, nonpharmacological approach to improve sleep through disruption of the DMN prior to sleep onset for individuals with insomnia. Primary outcome measures will include assessing changes in DMN functional connectivity before and after stimulation. METHODS: A total of 20 participants between the ages of 18 to 50 years with reported sleep disturbances will be recruited as a part of the study. Participants will then conduct an in-person screening and follow-on enrollment visit. Eligible participants then conduct at-home actigraphic collection until their first in-residence overnight study visit. In a double-blind, counterbalanced, crossover study design, participants will receive a 40-second stimulation to the left inferior parietal lobule of the DMN during 2 separate overnight in-residence visits. Participants are randomized to the order in which they receive the active stimulation and sham stimulation. Study participants will undergo a prestimulation functional magnetic resonance imaging scan and a poststimulation functional magnetic resonance imaging scan prior to sleep for each overnight study visit. Sleep outcomes will be measured using clinical polysomnography. After their first in-residence study visit, participants conduct another at-home actigraphic collection before returning for their second in-residence overnight study visit. RESULTS: Our study was funded in September 2020 by the Department of Defense (W81XWH2010173). We completed the enrollment of our target study population in the October 2022 and are currently working on neuroimaging processing and analysis. We aim to publish the results of our study by 2024. Primary neuroimaging outcome measures will be tested using independent components analysis, seed-to-voxel analyses, and region of interest to region of interest analyses. A repeated measures analysis of covariance (ANCOVA) will be used to assess the effects of active and sham stimulation on sleep variables. Additionally, we will correlate changes in functional connectivity to polysomnography-graded sleep. CONCLUSIONS: The presently proposed cTBS protocol is aimed at establishing the initial research outcomes of the effects of a single burst of cTBS on disrupting the network connectivity of the DMN to improve sleep. If effective, future work could determine the most effective stimulation sites and administration schedules to optimize this potential intervention for sleep problems. TRIAL REGISTRATION: ClinicalTrials.gov NCT04953559; https://clinicaltrials.gov/ct2/show/NCT04953559. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51212.

Neighborhood-level sleep health and childhood opportunities.

Objectives: Regional sleep differences may reflect other important indicators of health and well-being. Examining sleep health at the regional level can help inform policies to improve population health. We examined the relationship between neighborhood-level adult sleep health (modeled in this study via adult sleep duration) and other health metrics and multiple indicators of child-relevant opportunity. Methods: Data were obtained from the "500 Cities" data collected by the CDC, including the proportion of the adult population in each tract that report obtaining at least 7 h of sleep. The Child Opportunity Index (COI) provides indices for "education," "health and environment," and "social and economic" domains, as well as a global score. When data were merged, 27,130 census tracts were included. Linear regression analyses examined COI associated with the proportion of the adult population obtaining 7 h of sleep. Results: Adult sleep duration was associated with global COI, such that for each additional percent of the population that obtains ≥ 7 h of sleep, COI increases by 3.6 points (95%CI[3.57, 3.64]). Each component of COI was separately related to adult sleep duration. All associations were attenuated but significant in adjusted analyses. In stepwise analyses, sleep health via adult sleep duration emerged as the strongest correlate of global COI, accounting for 57.2% of the variance (p < 0.0001). Similarly, when stepwise analyses examined each component of COI as dependent variable, sleep health consistently emerged as the most substantial correlate (all p < 0.0001). Conclusion: Community levels of sufficient sleep are associated with greater childhood opportunities, which itself is robustly associated with a wide range of health and economic outcomes. Future work can examine whether this association can develop into scalable interventions.