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.

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.

Reduced anhedonia following internet-based cognitive-behavioral therapy for depression is mediated by enhanced reward circuit activation.

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent psychiatric condition, yet many patients do not receive adequate treatment. Novel and highly scalable interventions such as internet-based cognitive-behavioral-therapy (iCBT) may help to address this treatment gap. Anhedonia, a hallmark symptom of MDD that refers to diminished interest and ability to experience pleasure, has been associated with reduced reactivity in a neural reward circuit that includes medial prefrontal and striatal brain regions. Whether iCBT can reduce anhedonia severity in MDD patients, and whether these therapeutic effects are accompanied by enhanced reward circuit reactivity has yet to be examined. METHODS: Fifty-two MDD patients were randomly assigned to either 10-week iCBT (n = 26) or monitored attention control (MAC, n = 26) programs. All patients completed pre- and post-treatment assessments of anhedonia (Snaith-Hamilton Pleasure Scale; SHAPS) and reward circuit reactivity [monetary incentive delay (MID) task during functional magnetic resonance imaging (fMRI)]. Healthy control participants (n = 42) also underwent two fMRI scans while completing the MID task 10 weeks apart. RESULTS: Both iCBT and MAC groups exhibited a reduction in anhedonia severity post-treatment. Nevertheless, only the iCBT group exhibited enhanced nucleus accumbens (Nacc) and subgenual anterior cingulate cortex (sgACC) activation and functional connectivity from pre- to post-treatment in response to reward feedback. Enhanced Nacc and sgACC activations were associated with reduced anhedonia severity following iCBT treatment, with enhanced Nacc activation also mediating the reduction in anhedonia severity post-treatment. CONCLUSIONS: These findings suggest that increased reward circuit reactivity may contribute to a reduction in anhedonia severity following iCBT treatment for depression.

Nightmare content during the COVID-19 pandemic: Influence of COVID-related stress and sleep disruption in the United States.

Nightmares are often associated with psychiatric disorders and acute stress. This study explores how the COVID-19 pandemic may have influenced the content of nightmares. A sample of N = 419 US adults completed online surveys about sleep and COVID-19 experiences. Participants were asked about the degree to which they agreed with statements linking greater general stress, worse overall sleep and more middle-of-the-night insomnia with the COVID-19 pandemic. They were also asked if, during the pandemic, they experienced nightmares related to various themes. Logistic regression analyses examined each nightmare content as outcome and increased stress, worse sleep and more middle-of-the-night insomnia as predictors, adjusted for age, sex and race/ethnicity. Those who reported greater general COVID-related stress were more likely to have nightmares about confinement, failure, helplessness, anxiety, war, separation, totalitarianism, sickness, death, COVID and an apocalypse. Those who reported worsened sleep were more likely to have nightmares about confinement, oppression, failure, helplessness, disaster, anxiety, evil forces, war, domestic abuse, separation, totalitarianism, sickness, death, COVID and an apocalypse. Those who reported worsened middle-of-the-night insomnia were more likely to have nightmares about confinement, oppression, failure, helplessness, disaster, anxiety, war, domestic abuse, separation, totalitarianism, sickness, death, COVID and an apocalypse. These results suggest that increased pandemic-related stress may induce negatively-toned dreams of specific themes. Future investigation might determine whether (and when) this symptom indicates an emotion regulation mechanism at play, or the failure of such a mechanism.

Menstrual regularity and bleeding is associated with sleep duration, sleep quality and fatigue in a community sample.

Female menstrual health and its relationship with sleep is an understudied subject. The aim of this investigation was to determine the association between the two in a community sample. Data were obtained from n = 579 menstruating females who participated in the Sleep and Health Activity, Diet, Environment, and Socialization (SHADES) study, a community-based sample of adults aged 22-60 years. Participants were asked, "How regular is your period?", with response choices of "very regular", "mostly regular", "fairly regular" and "not regular". They were also asked, "How much bleeding do you usually experience during your period?" Response choices were: "very heavy", "heavy", "medium", "light" or "very light". These were evaluated as ordinal outcomes. Sleep-related predictors included sleep duration (in hr; ≤ 6 [short], 7-9 [normal] and ≥ 9 [long]), Insomnia Severity Index score, Pittsburgh Sleep Quality Index score, Epworth Sleepiness Scale score and Fatigue Severity Scale score. Covariates included age, education, income, race/ethnicity and body mass index. Short sleep duration was associated with heavier bleeding (odds ratio = 1.46, p = 0.026) and greater cycle irregularity (odds ratio = 1.44, p = 0.031) as compared with normal sleep. Higher Pittsburgh Sleep Quality Index score was associated with greater cycle irregularity (odds ratio = 1.05, p = 0.022). Higher Fatigue Severity Scale score was associated with heavier bleeding (odds ratio = 1.02, p = 0.003) and greater cycle irregularity (odds ratio = 1.02, p = 0.008). Long sleep, Insomnia Severity Index and Epworth Sleepiness Scale were not associated with either outcome. These results demonstrate an association between short sleep duration, poor sleep quality, fatigue, stress and depression with heavier bleeding and menstrual cycle irregularity, highlighting the need for further studies to improve treatment options.

Association between emotional intelligence and effective brain connectome: A large-scale spectral DCM study.

INTRODUCTION: Emotional Intelligence (EI) is a well-documented aspect of social and interpersonal functioning, but the underlying neural mechanisms for this capacity remain poorly understood. Here we used advanced brain connectivity techniques to explore the associations between EI and effective connectivity (EC) within four functional brain networks. METHODS: The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was used to collect EI data from 55 healthy individuals (mean age = 30.56±8.3 years, 26 males). The MSCEIT comprises two area cores - experiential EI (T1) and strategic EI (T2). The T1 core included two sub-scales - perception of emotions (S1) and using emotions to facilitate thinking (S2), and the T2 core included two sub-scales - understanding of emotions (S3) and management of emotions (S4). All participants underwent structural and resting-state functional magnetic resonance imaging (rsfMRI) scans. The spectral dynamic causal modeling approach was implemented to estimate EC within four networks of interest - the default-mode network (DMN), dorsal attention network (DAN), control-execution network (CEN) and salience network (SN). The strength of EC within each network was correlated with the measures of EI, with correlations at pFDR < 0.05 considered as significant. RESULTS: There was no significant association between any of the measures of EI and EC strength within the DMN and DAN. For CEN, however, we found that there were significant negative associations between EC strength from the right anterior prefrontal cortex (RAPFC) to the left anterior prefrontal cortex (LAPFC) and both S2 and T1, and significant positive associations between EC strength from LAPFC to RAPFC and S2. EC strength from the right superior parietal cortex (SPC) to RAPFC also showed significant negative association with S4 and T2. For the SN, S3 showed significant negative association with EC strength from the right insula to RAPFC and significant positive association with EC strength from the left insula to dorsal anterior cingulate cortex (DACC). CONCLUSIONS: We provide evidence that the negative ECs within the right hemisphere, and from the right to left hemisphere, and positive ECs within the left hemisphere and from the left to right hemisphere of CEN (involving bilateral frontal and right parietal region) and SN (involving right frontal, anterior cingulate and bilateral insula) play a significant role in regulating and processing emotions. These findings also suggest that measures of EC can be utilized as important biomarkers to better understand the underlying neural mechanisms of EI.

Emotional intelligence training as a protective factor for mental health during the COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic has presented a major challenge to mental health and emotional wellbeing. The present study examined whether training in emotional intelligence (EI) skills, provided before the pandemic, would serve as a protective factor for sustaining mental health during the COVID-19 crisis. METHODS: Data came from a longitudinal study (N = 89) that was initially designed to test the effectiveness of an EI training program versus a non-emotion-focused placebo program. The design and timing of the study were such that baseline and posttraining assessments of depression and anxiety had been completed before the pandemic, and planned 6-month follow-ups were serendipitously scheduled to occur after the onset of the COVID-19 crisis. This provided us with an unexpected real-world opportunity to investigate whether EI training would bolster emotional resilience to the challenges posed by the COVID-19 pandemic. RESULTS: Although mental health concerns generally increased after the start of the pandemic, individuals who completed the EI training program scored lower on depression, suicidal ideation, and state anxiety relative to individuals who had been assigned to the placebo training program. CONCLUSION: Online EI training appears to be effective at sustaining critical aspects of mental health during a subsequent real-life crisis.

Denoising scanner effects from multimodal MRI data using linked independent component analysis.

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.

A randomized, double-blind, placebo-controlled trial of blue wavelength light exposure on sleep and recovery of brain structure, function, and cognition following mild traumatic brain injury.

Sleep and circadian rhythms are among the most powerful but least understood contributors to cognitive performance and brain health. Here we capitalize on the circadian resetting effect of blue-wavelength light to phase shift the sleep patterns of adult patients (aged 18-48 years) recovering from mild traumatic brain injury (mTBI), with the aim of facilitating recovery of brain structure, connectivity, and cognitive performance. During a randomized, double-blind, placebo-controlled trial of 32 adults with a recent mTBI, we compared 6-weeks of daily 30-min pulses of blue light (peak λ = 469 nm) each morning versus amber placebo light (peak λ = 578 nm) on neurocognitive and neuroimaging outcomes, including gray matter volume (GMV), resting-state functional connectivity, directed connectivity using Granger causality, and white matter integrity using diffusion tensor imaging (DTI). Relative to placebo, morning blue light led to phase-advanced sleep timing, reduced daytime sleepiness, and improved executive functioning, and was associated with increased volume of the posterior thalamus (i.e., pulvinar), greater thalamo-cortical functional connectivity, and increased axonal integrity of these pathways. These findings provide insight into the contributions of the circadian and sleep systems in brain repair and lay the groundwork for interventions targeting the retinohypothalamic system to facilitate injury recovery.

Sex differences in limbic network and risk-taking propensity in healthy individuals.

Little is known about the structural neural substrates that may contribute to sex differences in risk-taking propensity (RTP). A close association between risk-seeking behavior and the emotional-regulation network led us to hypothesize that the sex differences in RTP would be associated with sex differences in brain morphometry of the limbic network (LN). We collected RTP scores using the bubble sheet version of the evaluation of risk (EVAR) scale and neuroanatomical data from 57 healthy individuals (29 males). The EVAR scale included sub-scales measuring recklessness/impulsivity, self-confidence, and need for control (NFC). We observed significant sex differences in NFC showing greater desire for control and dominance in males than females (multivariate analysis of covariance, MANCOVAN: p = .01). Morphometry analysis showed that it was only the right LN, which showed significant sex differences in normalized surface area, normalized cortical volume, and adjusted mean curvature index (females > males) at p < .01 (MANCOVAN, corrected for multiple comparisons). Correlation analysis showed that greater curvature of the right LN was significantly associated with lower desire for control in high-risk events (r = -.31, p = .02 at 95% CI of [-0.53, -0.05]). Our findings suggest that the normalized cortical measures could indicate specific sex differences in brain morphometry, particularly within the LN. The curvature index was the only differentiating factor for greater/lower propensity for risk-taking behavior in overall sample. Therefore, the LN and the curvature measures could be key biomarkers, which play an important role in predicting risk-taking behavior.

Daily Morning Blue Light Therapy Improves Daytime Sleepiness, Sleep Quality, and Quality of Life Following a Mild Traumatic Brain Injury.

OBJECTIVE: Identify the treatment effects of 6 weeks of daily 30-minute sessions of morning blue light therapy compared with placebo amber light therapy in the treatment of sleep disruption following mild traumatic brain injury. DESIGN: Placebo-controlled randomized trial. PARTICIPANTS: Adults aged 18 to 45 years with a mild traumatic brain injury within the past 18 months (n = 35). MAIN OUTCOME MEASURES: Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Beck Depression Inventory II, Rivermead Post-concussion Symptom Questionnaire, Functional Outcomes of Sleep Questionnaire, and actigraphy-derived sleep measures. RESULTS: Following treatment, moderate to large improvements were observed with individuals in the blue light therapy group reporting lower Epworth Sleepiness Scale (Hedges' g = 0.882), Beck Depression Inventory II (g = 0.684), Rivermead Post-concussion Symptom Questionnaire chronic (g = 0.611), and somatic (g = 0.597) symptoms, and experiencing lower normalized wake after sleep onset (g = 0.667) than those in the amber light therapy group. In addition, individuals in the blue light therapy group experienced greater total sleep time (g = 0.529) and reported improved Functional Outcomes of Sleep Questionnaire scores (g = 0.929) than those in the amber light therapy group. CONCLUSION: Daytime sleepiness, fatigue, and sleep disruption are common following a mild traumatic brain injury. These findings further substantiate blue light therapy as a promising nonpharmacological approach to improve these sleep-related complaints with the added benefit of improved postconcussion symptoms and depression severity.

Vulnerability to mood degradation during sleep deprivation is influenced by white-matter compactness of the triple-network model.

Sleep deprivation (SD) is often associated with significant shifts in mood state relative to baseline functioning. Prior work suggests that there are consistent trait-like differences among individuals in the degree to which their mood and performances are affected by sleep loss. The goal of this study was to determine the extent to which trait-like individual differences in vulnerability/resistance to mood degradation during a night of SD are dependent upon region-specific white and grey matter (WM/GM) characteristics of a triple-network model, including the default-mode network (DMN), control-execution network (CEN) and salience network (SN). Diffusion-weighted and anatomical brain data were collected from 45 healthy individuals several days prior to a 28-h overnight SD protocol. During SD, a visual analog mood scale was administered every hour from 19:15 (time point1; TP1) to 11:15 (TP17) the following morning to measure two positive and six negative mood states. Four core regions within the DMN, five within the CEN, and seven within the SN were used as regions of interest (ROIs). An index of mood resistance (IMR) was defined as the averaged differences between positive and negative mood states over 12 TPs (TP5 to TP16) relative to baseline (TP1 to TP4). For each ROI, characteristics of WM - quantitative anisotropy (QA) and mean curvature index (WM-MCI), and GM - cortical volume (CV) and GM-MCI were estimated, and used to predict IMR. WM characteristics, particularly QA, of all of regions within the DMN, and most of the regions within the CEN and SN predicted IMR during SD. In contrast, most ROIs did not show significant association between IMR and any of the GM characteristics (CV and MCI) or WM MCI. Our findings suggest that greater resilience to mood degradation induced by total SD appears to be associated with more compact axonal pathways within the DMN, CEN and SN.

An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease.

OBJECTIVE: Two distinct perspectives-typically referred to as the biopsychosocial and biomedical models-currently guide clinical practice. Although the role of psychosocial factors in contributing to physical and mental health outcomes is widely recognized, the biomedical model remains dominant. This is due in part to (a) the largely nonmechanistic focus of biopsychosocial research and (b) the lack of specificity it currently offers in guiding clinicians to focus on social, psychological, and/or biological factors in individual cases. In this article, our objective is to provide an evidence-based and theoretically sophisticated mechanistic model capable of organically integrating biopsychosocial processes. METHODS: To construct this model, we provide a narrative review of recent advances in embodied cognition and predictive processing within computational neuroscience, which offer mechanisms for understanding individual differences in social perceptions, visceral responses, health-related behaviors, and their interactions. We also review current evidence for bidirectional influences between social support and health as a detailed illustration of the novel conceptual resources offered by our model. RESULTS: When integrated, these advances highlight multiple mechanistic causal pathways between psychosocial and biological variables. CONCLUSIONS: By highlighting these pathways, the resulting model has important implications motivating a more psychologically sophisticated, person-specific approach to future research and clinical application in the biopsychosocial domain. It also highlights the potential for quantitative computational modeling and the design of novel interventions. Finally, it should aid in guiding future research in a manner capable of addressing the current criticisms/limitations of the biopsychosocial model and may therefore represent an important step in bridging the gap between it and the biomedical perspective.

The Association Between Trait Gratitude and Self-Reported Sleep Quality Is Mediated by Depressive Mood State.

BACKGROUND: It has been shown that higher levels of trait gratitude are associated with better self-reported sleep quality, possibly due to differences in presleep cognitions. However previous studies have not taken into account the role of depressive symptoms in this relationship. PARTICIPANTS AND METHODS: In this study, 88 nonclinical 18-29-year-olds completed the Gratitude Resentment and Appreciation Test (GRAT) as a measure of trait gratitude. The Glasgow Content of Thought Inventory (GCTI) was used to measure the intrusiveness of cognitions prior to sleep onset, the Motivation and Energy Inventory (MEI) assessed daytime fatigue, and the Pittsburgh Sleep Quality Index (PSQI) was used to assess self-reported sleep quality. The BDI-II assessed self-reported depressive symptoms. RESULTS: Consistent with previous work, GRAT scores were positively associated with higher daytime energy and greater number of hours of sleep per night. Importantly, however, we further observed that depressive symptoms mediated the relationships between gratitude scores and sleep metrics. CONCLUSIONS: Depressive mood state appears to mediate the association between gratitude and self-reported sleep quality metrics. We suggest, as one plausible model of these phenomena, that highly grateful individuals have lower symptoms of depression, which in turn leads to fewer presleep worries, resulting in better perceived sleep quality. Future work should aim to disentangle the causal nature of these relationships in order to better understand how these important variables interact.

Time-dependent differences in cortical measures and their associations with behavioral measures following mild traumatic brain injury.

There is currently a critical need to establish an improved understanding of time-dependent differences in brain structure following mild traumatic brain injury (mTBI). We compared differences in brain structure, specifically cortical thickness (CT), cortical volume (CV), and cortical surface area (CSA) in 54 individuals who sustained a recent mTBI and 33 healthy controls (HCs). Individuals with mTBI were split into three groups, depending on their time since injury. By comparing structural measures between mTBI and HC groups, differences in CT reflected cortical thickening within several areas following 0-3 (time-point, TP1) and 3-6 months (TP2) post-mTBI. Compared with the HC group, the mTBI group at TP2 showed lower CSA within several areas. Compared with the mTBI group at TP2, the mTBI group during the most chronic stage (TP3: 6-18 months post-mTBI) showed significantly higher CSA in several areas. All the above reported differences in CT and CSA were significant at a cluster-forming p < .01 (corrected for multiple comparisons). We also found that in the mTBI group at TP2, CT within two clusters (i.e., the left rostral middle frontal gyrus (L. RMFG) and the right postcentral gyrus (R. PostCG)) was negatively correlated with basic attention abilities (L. RMFG: r = -.41, p = .05 and R. PostCG: r = -.44, p = .03). Our findings suggest that alterations in CT and associated neuropsychological assessments may be more prominent during the early stages of mTBI. However, alterations in CSA may reflect compensatory structural recovery during the chronic stages of mTBI.

Nested positive feedback loops in the maintenance of major depression: An integration and extension of previous models.

Several theories of Major Depressive Disorder (MDD) have previously been proposed, focusing largely on either a psychological (i.e., cognitive/affective), biological, or neural/computational level of description. These theories appeal to somewhat distinct bodies of work that have each highlighted separate factors as being of considerable potential importance to the maintenance of MDD. Such factors include a range of cognitive/attentional information-processing biases, a range of structural and functional brain abnormalities, and also dysregulation within the autonomic, endocrine, and immune systems. However, to date there have been limited efforts to integrate these complimentary perspectives into a single multi-level framework. Here we review previous work in each of these MDD research domains and illustrate how they can be synthesized into a more comprehensive model of how a depressive episode is maintained. In particular, we emphasize how plausible (but insufficiently studied) interactions between the various MDD-related factors listed above can lead to a series of nested positive feedback loops, which are each capable of maintaining an individual in a depressive episode. We also describe how these different feedback loops could be active to different degrees in different individual cases, potentially accounting for heterogeneity in both depressive symptoms and treatment response. We conclude by discussing how this integrative model might extend understanding of current treatment mechanisms, and also potentially guide the search for markers to inform treatment selection in individual cases.

Chronic sleep restriction differentially affects implicit biases toward food among men and women: preliminary evidence.

Chronic sleep restriction and obesity are two major public health concerns. This study investigated how chronic sleep restriction changes implicit attitudes towards low- and high-calorie foods. In a randomized, counterbalanced cross-over design, 17 participants (eight females, nine males) underwent two laboratory testing sessions where they were either sleep-restricted for 3 weeks (i.e. underwent three weekly cycles of 5 nights of 4 h of sleep followed by 2 nights of 8 h of sleep opportunity) or received 3 weeks of control sleep (i.e. 8 h of sleep opportunity per night for 3 weeks). There was evidence for a significant sleep condition x sex interaction (F(1, 20)  = 4.60, P = 0.04). After chronic sleep restriction, men showed a trend towards a significant decrease in their implicit attitudes favouring low-calorie foods (P = 0.08), whereas women did not show a significant change (P = 0.16). Men may be at increased risk of weight gain when sleep-deprived due to a reduced bias towards low-calorie foods.

Greater cortical thickness within the limbic visceromotor network predicts higher levels of trait emotional awareness.

Previous studies of trait emotional awareness (EA) have not yet examined whether differences in cortical structure might account for differences in EA. Based on previous research on the relationship between EA and both emotion conceptualization and visceromotor control processes, we tested two hypotheses in a sample of 26 healthy participants: that higher EA would be predicted by greater cortical thickness within (1) regions of the default mode network (DMN; linked with conceptualization processes), and/or (2) regions of the limbic network (linked with affect generation and visceromotor control processes). A non-significant correlation was found between EA and cortical thickness in the DMN. In contrast, a significant positive correlation was observed between EA and cortical thickness within the limbic network. These findings suggest that the structural integrity of cortical regions involved in the generation of affective bodily reactions may play a more important role in explaining differences in EA than previously thought.

Resting state functional connectivity correlates of emotional awareness.

Multiple neuroimaging studies have now linked emotional awareness (EA), as measured by the Levels of Emotional Awareness Scale (LEAS), with activation in regions of neural networks associated with both conceptualization (i.e., default mode network [DMN] regions) and interoception (i.e., salience network [SN] regions) - consistent with the definition of EA as one's ability to appropriately recognize, conceptualize, and articulate the emotions of self and other in fine-grained, differentiated ways. However, no study has yet tested the hypothesis that greater LEAS scores are associated with greater resting state functional connectivity (FC) within these networks. Twenty-six adults (13 female) underwent resting state functional magnetic resonance imaging, and also completed the LEAS. Using pre-defined functional ROIs from the DMN and SN, we observed that LEAS scores were significantly positively correlated with FC between several regions of both of these networks, even when controlling for differences in general intelligence (IQ). These results suggest that higher EA may be associated with more efficient information exchange between brain regions involved in both interoception- and conceptualization-based processing, which could plausibly contribute to more differentiated bodily feelings and more fine-grained conceptualization of those feelings.

Internet-based cognitive behavior therapy for major depressive disorder: A randomized controlled trial.

BACKGROUND: Prior research has shown that the Sadness Program, a technician-assisted Internet-based cognitive behavioral therapy (iCBT) intervention developed in Australia, is effective for treating major depressive disorder (MDD). The current study aimed to expand this work by adapting the protocol for an American population and testing the Sadness Program with an attention control group. METHODS: In this parallel-group, randomized controlled trial, adult MDD participants (18-45 years) were randomized to a 10-week period of iCBT (n = 37) or monitored attention control (MAC; n = 40). Participants in the iCBT group completed six online therapy lessons, which included access to content summaries and homework assignments. During the 10-week trial, iCBT and MAC participants logged into the web-based system six times to complete self-report symptom scales, and a nonclinician technician contacted participants weekly to provide encouragement and support. The primary outcome was the Hamilton Rating Scale for Depression (HRSD), and the secondary outcomes were the Patient Health Questionnaire-9 and Kessler-10. RESULTS: Intent-to-treat analyses revealed significantly greater reductions in depressive symptoms in iCBT compared with MAC participants, using both the self-report measures and the clinician-rated HRSD (d = -0.80). Importantly, iCBT participants also showed significantly higher rates of clinical response and remission. Exploratory analyses did not support illness severity as a moderator of treatment outcome. CONCLUSIONS: The Sadness Program led to significant reductions in depression and distress symptoms. With its potential to be delivered in a scalable, cost-efficient manner, iCBT is a promising strategy to enhance access to effective care.