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.

Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach.

BACKGROUND: Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question. METHODS: Adolescent participants (n = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2-3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage. RESULTS: A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%). CONCLUSIONS: To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief 'just-in-time' interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.

Multi-modal assessment of reward functioning in adolescent anhedonia.

BACKGROUND: Anhedonia is a core symptom of depression that predicts worse treatment outcomes. Dysfunction in neural reward circuits is thought to contribute to anhedonia. However, whether laboratory-based assessments of anhedonia and reward-related neural function translate to adolescents' subjective affective experiences in real-world contexts remains unclear. METHODS: We recruited a sample of adolescents (n = 82; ages 12-18; mean = 15.83) who varied in anhedonia and measured the relationships among clinician-rated and self-reported anhedonia, behaviorally assessed reward learning ability, neural response to monetary reward and loss (as assessed with functional magnetic resonance imaging), and repeated ecological momentary assessment (EMA) of positive affect (PA) and negative affect (NA) in daily life. RESULTS: Anhedonia was associated with lower mean PA and higher mean NA across the 5-day EMA period. Anhedonia was not related to impaired behavioral reward learning, but low PA was associated with reduced nucleus accumbens response during reward anticipation and reduced medial prefrontal cortex (mPFC) response during reward outcome. Greater mean NA was associated with increased mPFC response to loss outcome. CONCLUSIONS: Traditional laboratory-based measures of anhedonia were associated with lower subjective PA and higher subjective NA in youths' daily lives. Lower subjective PA and higher subjective NA were associated with decreased reward-related striatal functioning. Higher NA was also related to increased mPFC activity to loss. Collectively, these findings demonstrate that laboratory-based measures of anhedonia translate to real-world contexts and that subjective ratings of PA and NA may be associated with neural response to reward and loss.

Randomized Controlled Trial of a Mindfulness Mobile Application for Ruminative Adolescents.

OBJECTIVE: Rumination is a risk factor for the development of internalizing psychopathology that often emerges during adolescence. The goal of the present study was to test a mindfulness mobile app intervention designed to reduce rumination. METHOD: Ruminative adolescents (N = 152; 59% girls, 18% racial/ethnic minority, Mage = 13.72, SD = .89) were randomly assigned to use a mobile app 3 times per day for 3 weeks that delivered brief mindfulness exercises or a mood monitoring-only control. Participants reported on rumination, depressive symptoms and anxiety symptoms at baseline, post-intervention and at 3 follow-up timepoints: 6 weeks, 12 weeks, and 6 months post-intervention. Parents reported on internalizing symptoms. RESULTS: There was a significant Time X Condition effect at post-intervention for rumination, depressive symptoms, and anxiety symptoms, such that participants in the mindfulness intervention showed improvements relative to those in the control condition. The effect for rumination lasted through the 6-week follow-up period; however, group differences were generally not observed throughout the follow-up period, which may indicate that continued practice is needed for gains to be maintained. CONCLUSIONS: This intervention may have the potential to prevent the development of psychopathology and should be tested in a longitudinal study assessing affective disorder onset, especially in populations with limited access to conventional, in person mental health care.This study was registered with Clinicaltrials.gov (Identifier NCT03900416).

Exploration of baseline and early changes in neurocognitive characteristics as predictors of treatment response to bupropion, sertraline, and placebo in the EMBARC clinical trial.

BACKGROUND: Treatment for major depressive disorder (MDD) is imprecise and often involves trial-and-error to determine the most effective approach. To facilitate optimal treatment selection and inform timely adjustment, the current study investigated whether neurocognitive variables could predict an antidepressant response in a treatment-specific manner. METHODS: In the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial, outpatients with non-psychotic recurrent MDD were first randomized to an 8-week course of sertraline selective serotonin reuptake inhibitor or placebo. Behavioral measures of reward responsiveness, cognitive control, verbal fluency, psychomotor, and cognitive processing speeds were collected at baseline and week 1. Treatment responders then continued on another 8-week course of the same medication, whereas non-responders to sertraline or placebo were crossed-over under double-blinded conditions to bupropion noradrenaline/dopamine reuptake inhibitor or sertraline, respectively. Hamilton Rating for Depression scores were also assessed at baseline, weeks 8, and 16. RESULTS: Greater improvements in psychomotor and cognitive processing speeds within the first week, as well as better pretreatment performance in these domains, were specifically associated with higher likelihood of response to placebo. Moreover, better reward responsiveness, poorer cognitive control and greater verbal fluency were associated with greater likelihood of response to bupropion in patients who previously failed to respond to sertraline. CONCLUSION: These exploratory results warrant further scrutiny, but demonstrate that quick and non-invasive behavioral tests may have substantial clinical value in predicting antidepressant treatment response.

Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.

BACKGROUND: Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps. OBJECTIVE: This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training. METHODS: Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a "Personalized Advantage Index" (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control. RESULTS: A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit. CONCLUSIONS: Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them. TRIAL REGISTRATION: ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.

Preliminary Evidence for Sociotropy and Autonomy in Relation to Antidepressant Treatment Outcome.

Sociotropy and autonomy are cognitive-personality styles that have been hypothesized to confer vulnerability to different presentations of major depressive disorder (MDD), which may respond differentially to treatment. Specifically, the profile of low sociotropy and high autonomy is hypothesized to indicate a positive response to antidepressant medication. The current study examines sociotropy and autonomy in relation to sertraline treatment response in individuals with MDD. As part of an ancillary study to the larger Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) project, individuals with MDD participated in an 8-week trial of sertraline and completed a self-report questionnaire of sociotropy and autonomy. Discriminant function analyses were used to examine whether sociotropy and autonomy scores could distinguish antidepressant treatment responders (determined by a 50% or greater reduction in depressive symptoms) from non-responders. The sociotropy scale successfully discriminated sertraline treatment responders from non-responders. Further, lower sociotropy was associated with greater improvements in depressive symptomology following sertraline treatment. The current findings suggest individuals with MDD characterized by low sociotropy are more likely to benefit from sertraline. Given the promising results of the Sociotropy-Autonomy Scale in discriminating treatment responders from non-responders, the low resources necessary for administration, and the ease of translation into routine clinical care, the scale warrants further research attention.

Dissecting the impact of depression on decision-making.

BACKGROUND: Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression. METHODS: Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters. RESULTS: RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced-and decision thresholds were wider-in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did. CONCLUSIONS: Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making-not slowed perception or response execution-caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits. METHODS: Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics. RESULTS: Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58). CONCLUSIONS: A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

Wheat Virus Identification Within Infected Tissue Using Nanopore Sequencing Technology.

Viral diseases are a limiting factor to wheat production. Viruses are difficult to diagnose in the early stages of disease development and are often confused with nutrient deficiencies or other abiotic problems. Immunological methods are useful to identify viruses, but specific antibodies may not be available or require high virus titer for detection. In 2015 and 2017, wheat plants containing Wheat streak mosaic virus (WSMV) resistance gene, Wsm2, were found to have symptoms characteristic of WSMV. Serologically, WSMV was detected in all four samples. Additionally, High Plains wheat mosaic virus (HPWMoV) was also detected in one of the samples. Barley yellow dwarf virus (BYDV) was not detected, and a detection kit was not readily available for Triticum mosaic virus (TriMV). Initially, cDNA cloning and Sanger sequencing were used to determine the presence of WSMV; however, the process was time-consuming and expensive. Subsequently, cDNA from infected wheat tissue was sequenced with single-strand, Oxford Nanopore sequencing technology (ONT). ONT was able to confirm the presence of WSMV. Additionally, TriMV was found in all of the samples and BYDV in three of the samples. Deep coverage sequencing of full-length, single-strand WSMV revealed variation compared with the WSMV Sidney-81 reference strain and may represent new variants which overcome Wsm2. These results demonstrate that ONT can more accurately identify causal virus agents and has sufficient resolution to provide evidence of causal variants.

A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial.

BACKGROUND: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. METHODS: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. RESULTS: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. CONCLUSION: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.

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.

The role of cognitive versus emotional intelligence in Iowa Gambling Task performance: What's emotion got to do with it?

Debate persists regarding the relative role of cognitive versus emotional processes in driving successful performance on the widely used Iowa Gambling Task (IGT). From the time of its initial development, patterns of IGT performance were commonly interpreted as primarily reflecting implicit, emotion-based processes. Surprisingly, little research has tried to directly compare the extent to which measures tapping relevant cognitive versus emotional competencies predict IGT performance in the same study. The current investigation attempts to address this question by comparing patterns of associations between IGT performance, cognitive intelligence (Wechsler Abbreviated Scale of Intelligence; WASI) and three commonly employed measures of emotional intelligence (EI; Mayer-Salovey-Caruso Emotional Intelligence Test, MSCEIT; Bar-On Emotional Quotient Inventory, EQ-i; Self-Rated Emotional Intelligence Scale, SREIS). Results indicated that IGT performance was more strongly associated with cognitive, than emotional, intelligence. To the extent that the IGT indeed mimics "real-world" decision-making, our findings, coupled with the results of existing research, may highlight the role of deliberate, cognitive capacities over implicit, emotional processes in contributing to at least some domains of decision-making relevant to everyday life.

Identifying who benefits most from supportive versus expressive techniques in psychotherapy for depression: Moderators of within- versus between-individual effects.

OBJECTIVE: A recent randomized controlled trial (RCT) indicated that individuals with higher levels of attachment anxiety exhibited better treatment outcomes in supportive-expressive therapy (SET) relative to supportive therapy (ST). But to gain insight into within-patient therapeutic changes, a within-individual design is required. The present study contrasts previous findings based on theory-driven between-patient moderators with data-driven moderators of within-patient processes to investigate whether findings converge or diverge across these two approaches. METHOD: We used data of 118 patients from the pilot and active phases of a recent RCT for patients with major depressive disorder, comparing ST with SET, a time-limited psychodynamic therapy. The predefined primary outcome measure was the Hamilton Rating Scale for Depression. Supportive versus expressive techniques were rated based on patients' end-of-session perspective. We compared previous findings based on moderators of between-patient effects with a data-driven approach for identifying moderators of within-patient effects of techniques on subsequent outcome. RESULTS: After false discovery rate corrections, of 10 preselected moderators, patients' attachment anxiety and domineering style remained significant. Of these, bootstrap resampling revealed significant differences between ST and SET techniques for the attachment anxiety moderator: Those with higher attachment anxiety benefited more from greater use of ST than SET techniques in a particular session, as evidenced by lower levels of symptoms at the subsequent session. CONCLUSIONS: Our within-individual findings diverge from previously published between-individual analyses. This proof-of-concept study demonstrates the importance of complementing between-individuals with within-individual analyses to achieve better understanding of who benefits most from specific treatment techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Revealing subgroup-specific mechanisms of change via moderated mediation: A meditation intervention example.

OBJECTIVE: Effective psychosocial interventions exist for numerous mental health conditions. However, despite decades of research, limited progress has been made in clarifying the mechanisms that account for their beneficial effects. We know that many treatments work, but we know relatively little about why they work. Mechanisms of change may be obscured due to prior research collapsing across heterogeneous subgroups of patients with differing underlying mechanisms of response. Studies identifying baseline individual characteristics that predict differential response (i.e., moderation) may inform research on why (i.e., mediation) a particular subgroup has better outcomes to an intervention via tests of moderated mediation. METHOD: In a recent randomized controlled trial comparing a 4-week meditation app with a control condition in school system employees (N = 662), we previously developed a "Personalized Advantage Index" (PAI) using baseline characteristics, which identified a subgroup of individuals who derived relatively greater benefit from meditation training. Here, we tested whether the effect of mindfulness acquisition in mediating group differences in outcome was moderated by PAI scores. RESULTS: A significant index of moderated mediation (IMM = 1.22, 95% CI [0.30, 2.33]) revealed that the effect of mindfulness acquisition in mediating group differences in outcome was only significant among those individuals with PAI scores predicting relatively greater benefit from the meditation app. CONCLUSIONS: Subgroups of individuals may differ meaningfully in the mechanisms that mediate their response to an intervention. Considering subgroup-specific mediators may accelerate progress on clarifying mechanisms of change underlying psychosocial interventions and may help inform which specific interventions are most beneficial for whom. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multi-method assessment of suicidal thoughts and behaviors among patients in treatment for OCD and related disorders.

Obsessive-compulsive and related disorders (OCRDs) are associated with increased risk of suicidal thoughts and behaviors (STBs), yet research characterizing suicidality in OCRDs remains limited. A major challenge in assessing STBs is the reliance on explicit self-report. This study utilized multi-method assessment to examine changes in both implicit and explicit STBs in 31 adults receiving partial/residential treatment for OCRDs. Assessments were administered at admission and weekly during treatment. Approximately three-quarters of participants reported lifetime suicidal thoughts, with 16 % reporting a prior suicide attempt. OCD severity was significantly correlated with lifetime suicidal thoughts, and was significantly higher for those with lifetime suicidal thoughts and prior attempts compared to those without. Implicit biases towards death were not associated with OCD severity, and did not predict explicitly endorsed STBs. This is the first study to measure both explicit and implicit STBs in adults with OCRDs. Limitations included small sample size and lack of racial/ethnic diversity. Given the majority had recent suicidal thoughts and one in six had a prior attempt, we emphasize the importance of STB assessment in OCD treatment settings.

Habitual 'sleep credit' is associated with greater grey matter volume of the medial prefrontal cortex, higher emotional intelligence and better mental health.

In modern society, people often fail to obtain the amount of sleep that experts recommend for good health and performance. Insufficient sleep can lead to degraded cognitive performance and alterations in emotional functioning. However, most people also acknowledge that on a regular basis they obtain more sleep than they subjectively perceive they need at a minimum to stave off performance decrements, a construct we describe as subjective 'sleep credit'. Few people would contest the notion that getting more sleep is better, but data on both behavioural and neuroanatomical correlates of 'sleep credit' are surprisingly limited. We conducted a voxel-based morphometric study to assess cerebral grey matter correlates of habitually sleeping more than one's subjective requirements. We further tested whether these structural correlates are associated with perceived emotional intelligence and indices of psychopathology while controlling for age, gender, and total intracranial volume. In a sample of 55 healthy adults aged 18-45 years (28 males, 27 females), whole-brain multiple regression showed that habitual subjective 'sleep credit' was correlated positively with grey matter volume within regions of the left medial prefrontal cortex and right orbitofrontal gyrus. Volumes were extracted and regressed against self-report emotion and psychopathology indices. Only grey matter volume of the medial prefrontal cortex cluster correlated with greater emotional intelligence and lower scores on several indices of psychopathology. Findings converge with previous evidence of the role of the medial prefrontal cortex in the relationship between sleep and emotional functioning, and suggest that behaviour and brain structure vary with habitual 'sleep credit'.

Dynamic processes in behavioral activation therapy for anhedonic adolescents: Modeling common and patient-specific relations.

OBJECTIVE: Behavioral activation (BA) is a brief intervention for depression encouraging gradual and systematic re-engagement with rewarding activities and behaviors. Given this treatment focus, BA may be particularly beneficial for adolescents with prominent anhedonia, a predictor of poor treatment response and common residual symptom. We applied group iterative multiple model estimation (GIMME) to ecological momentary assessment (EMA) treatment data to investigate common and person-specific processes during BA for anhedonic adolescents. METHOD: Thirty-nine adolescents (Mage = 15.7 years old, 67% female, 81% White) with elevated anhedonia (Snaith-Hamilton Pleasure Scale) were enrolled in a 12-week BA trial, with weekly anhedonia assessments. EMA surveys were triggered every other week (2-3 surveys per day) throughout treatment assessing current positive affect (PA) and negative affect (NA), engagement in pleasurable activities and social interactions, anticipatory pleasure, rumination, and recent pleasurable and stressful experiences. RESULTS: A multilevel model revealed significant decreases in anhedonia, t(25.5) = -4.76, p < .001, over the 12-week trial. GIMME results indicated substantial heterogeneity in variable networks across patients. PA was the variable with the greatest number (22% of all paths vs. 11% for NA) of predictive paths to other symptoms (i.e., highest out-degree). Higher PA (but not NA) out-degree was associated with greater anhedonia improvement, t(25.8) = -2.22, p = .035. CONCLUSIONS: Results revealed substantial heterogeneity in variable relations across patients, which may obscure the search for common processes of change in BA. PA may be a particularly important treatment target for anhedonic adolescents in BA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting Fear Extinction in Posttraumatic Stress Disorder.

Fear extinction is the basis of exposure therapies for posttraumatic stress disorder (PTSD), but half of patients do not improve. Predicting fear extinction in individuals with PTSD may inform personalized exposure therapy development. The participants were 125 trauma-exposed adults (96 female) with a range of PTSD symptoms. Electromyography, electrocardiogram, and skin conductance were recorded at baseline, during dark-enhanced startle, and during fear conditioning and extinction. Using a cross-validated, hold-out sample prediction approach, three penalized regressions and conventional ordinary least squares were trained to predict fear-potentiated startle during extinction using 50 predictor variables (5 clinical, 24 self-reported, and 21 physiological). The predictors, selected by penalized regression algorithms, were included in multivariable regression analyses, while univariate regressions assessed individual predictors. All the penalized regressions outperformed OLS in prediction accuracy and generalizability, as indexed by the lower mean squared error in the training and holdout subsamples. During early extinction, the consistent predictors across all the modeling approaches included dark-enhanced startle, the depersonalization and derealization subscale of the dissociative experiences scale, and the PTSD hyperarousal symptom score. These findings offer novel insights into the modeling approaches and patient characteristics that may reliably predict fear extinction in PTSD. Penalized regression shows promise for identifying symptom-related variables to enhance the predictive modeling accuracy in clinical research.

Reward-related predictors of symptom change in behavioral activation therapy for anhedonic adolescents: a multimodal approach.

Anhedonia is a cardinal characteristic of depression which predicts worse treatment outcome and is among the most common residual symptoms following treatment. Behavioral Activation (BA) has been shown to be an effective treatment for depressed adults, and more recently, depressed adolescents. Given its emphasis on systematically and gradually increasing exposure to and engagement with rewarding activities and experiences, BA may be a particularly effective intervention for adolescents experiencing anhedonia and associated reward system dysfunction. In the present study, anhedonic adolescents (AA; n = 39) received 12 weekly sessions of BA and completed a multimodal (i.e., neural, behavioral, and self-report [ecological momentary assessment]) assessment of reward function at pre-treatment and post-treatment (as well as weekly self-report assessments of anhedonia). Typically developing adolescents (TDA; n = 41) completed the same measures at corresponding timepoints. Multilevel models tested pre-treatment reward-related predictors of anhedonia improvement, as well as change in reward measures over the course of BA. Analyses revealed significant reductions in anhedonia following BA treatment. Enhanced pre-treatment neural (striatal) reward responsiveness predicted greater anhedonia improvement. In contrast, baseline self-report and behavioral reward measures did not predict treatment outcome. A group x time interaction revealed greater increases in both reward- and loss-related neural responsiveness among AA relative to TDA adolescents. Consistent with a capitalization (rather than compensatory) model, pre-treatment neural - but not self-report or behavioral - measures of relatively enhanced reward responsiveness predicted better BA outcome. In addition to alleviating anhedonia, successful BA may also increase neural sensitivity to affectively salient (e.g., reward- and loss-related) stimuli among anhedonic youth.