A smartphone app-based mindfulness intervention to enhance recovery from substance use disorders: Protocol for a pilot feasibility randomized controlled trial.

Background: Poor long-term recovery outcomes after treatment (e.g., readmission to inpatient treatment) are common among individuals with substance use disorders (SUDs). In-person mindfulness-based treatments (MBTs) are efficacious for SUDs and may improve recovery outcomes. However, existing MBTs for SUD have limited public health reach, and thus scalable delivery methods are needed. A digitally-delivered MBT for SUDs may hold promise. Methods: We recently developed Mindful Journey, a smartphone app-based adjunctive MBT for improving long-term recovery outcomes. In this paper, we present details on the app and describe the protocol for a single-site pilot feasibility randomized controlled trial of Mindful Journey. In this trial, individuals (n = 34) in an early phase of outpatient treatment for SUDs will be randomized to either treatment-as-usual (TAU) plus Mindful Journey, or TAU only. The trial will focus on testing the feasibility (e.g., engagement) and acceptability of the app (e.g., perceived usability and helpfulness for recovery), as well as feasibility of study procedures (e.g., assessment completion). The trial will incorporate ecological momentary assessment before and after treatment to assess mechanisms in real-time, including mindfulness, craving, difficulties with negative emotion regulation, and savoring. To examine the sensitivity to change of outcomes (substance use, substance-related problems, and psychological distress) and mechanism variables (noted above), we will test within-treatment-condition changes over time. Discussion: The proposed pilot trial will provide important preliminary data on whether Mindful Journey is feasible and acceptable among individuals with SUDs. Trial registration: ClinicalTrials.gov NCT05109507.

Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment.

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

Dynamics between affect and social acceptance as a function of social anxiety: A person-specific network approach.

Social acceptance and rejection are salient experiences, especially during adolescence. Acceptance and rejection relate to changes in positive and negative affect, although directionality of the relation remains unclear. The ability to regulate affect following social experiences may be part of the etiology of social anxiety disorder. With the importance of social cues in adolescence, as well as adolescence as a key window for the onset of social anxiety, we used daily diary data collected in a sample ranging from 9 to 18 years to examine daily changes in acceptance, rejection, positive affect, and negative affect. Taking a person-centered approach, we constructed networks directionally linking social experiences and affect, which served as behaviors of interest ("nodes") in the network for each individual. From these networks, we extracted recovery times from different nodes, that is, the number of days it took for a node to return to baseline when (a) the node itself was perturbed and (b) when a connected node was perturbed. We examined associations between network metrics and social anxiety, age, gender, and their interaction. We found that the recovery time of positive affect when social acceptance was perturbed was inversely related with social anxiety and age, suggesting benefits of acceptance may be shorter lasting for those with more (vs. less) social anxiety symptoms and for older (vs. younger) adolescents. We conclude that positive affect may be a critical yet understudied piece in understanding why adolescence is a developmental period of increased risk for psychopathology and for understanding the etiology of social anxiety disorder. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

It's a balancing act: The ratio of maladaptive (vs. All) emotion regulation strategies is associated with depression.

Research on emotion regulation (ER) has increasingly recognized that people use multiple strategies simultaneously, often referred to as ER repertoire. Prior research found that ER repertoire is associated with psychopathology, but results have been mixed. Indeed, research from recent years suggests that it is the quality of ERs, more than their quantity, that needs to be considered. Based on the combination of the literatures on ER repertoire, polyregulation, and ER flexibility, we propose a novel metric: the ratio of using putatively maladaptive (vs. all) ER strategies. Using this metric, we examine (1) maladaptive ER ratio changes during the transition to adolescence, a developmental period in which the prevalence of depression sharply increases, and (2) whether changes in maladaptive ER ratio are associated with depressive symptoms. One-hundred and thirty-nine youths (baseline age: 8-15) reported ER strategies and depression daily for 21 days. One year later, 115 completed another 28-day daily-diary (Nassessments = 5631). Our results show that almost all youth use at least some maladaptive ERs. Importantly, maladaptive ER ratio decreases over a year of adolescence for most youths. Conversely, an increased maladaptive ER ratio predicted depression increases on the daily and on the yearly level. These results shed light on typical and atypical development of ER flexibility and emphasize the need to consider the balance between ERs in relation to psychopathology.

Back to Normal? Harnessing Long Short-term Memory Network to Examine the Associations Between Adolescent Social Interactions and Depressive Symptoms During Different Stages of COVID-19.

Adolescence is a developmental period in which social interactions are critical for mental health. While the onset of COVID-19 significantly disrupted adolescents' social environments and mental health, it remains unclear how adolescents have adapted to later stages of the pandemic. We harnessed a machine learning architecture of Long Short-Term Memory recurrent networks (LSTM) with gradient-based feature importance, to model the association among daily social interactions and depressive symptoms during three stages of the pandemic. A year before COVID-19, 148 adolescents reported social interactions and depressive symptoms, every day for 21 days. One hundred sixteen of these youths completed a 28-day diary after schools closed due to COVID-19. Seventy-nine of these youths and additional 116 new participants completed a 28-day diary approximately a year into the pandemic. Our results show that LSTM successfully predicted depressive symptoms from at least a week of social interactions for all three waves (r2 > .70). Our study shows the utility of using an analytic approach that can identify temporal and nonlinear pathways through which social interactions may confer risk for depression. Our unique analysis of the importance of input features enabled us to interpret the association between social interactions and depressive symptoms. Collectively, we observed a return to pre-pandemic patterns a year into the pandemic, with reduced gender and age differences during the pandemic closures. This pattern suggests that the system of social influences in adolescence was affected by COVID-19, and that this effect was attenuated in more chronic stages of the pandemic.

Neuromarkers in addiction: definitions, development strategies, and recent advances.

Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.

Development and initial testing of mindful journey: a digital mindfulness-based intervention for promoting recovery from Substance use disorder.

BACKGROUND/OBJECTIVES: There is a great unmet need for accessible adjunctive interventions to promote long-term recovery from substance use disorder (SUD). This study aimed to iteratively develop and test the initial feasibility and acceptability of Mindful Journey, a novel digital mindfulness-based intervention for promoting recovery among individuals with SUD. PATIENTS/MATERIALS: Ten adults receiving outpatient treatment for SUD. METHODS: Phase 1 (n = 5) involved developing and testing a single introductory digital lesson. Phase 2 included a separate sample (n = 5) and involved testing all 15 digital lessons (each 30- to 45-minutes) over a 6-week period, while also receiving weekly brief phone coaching for motivational/technical support. RESULTS: Across both phases, quantitative ratings (rated on a 5-point scale) were all at or above a 4 (corresponding with 'agree') for key acceptability dimensions, such as usability, understandability, appeal of visual content, how engaging the content was, and helpfulness for recovery. Additionally, in both phases, qualitative feedback indicated that participants particularly appreciated the BOAT (Breath, Observe, Accept, Take a Moment) tool for breaking down mindfulness into steps. Qualitative feedback was used to iteratively refine the intervention. For example, based on feedback, we added a second core mindfulness tool, the SOAK (Stop, Observe, Appreciate, Keep Curious), and we added more example clients and group therapy videos. In Phase 2, 4 out of 5 participants completed all 15 lessons, providing initial evidence of feasibility. Participants reported that the phone coaching motivated them to use the app. The final version of Mindful Journey was a smartphone app with additional features, including brief on-the-go audio exercises and a library of mindfulness practices. Although, participants used these additional features infrequently. CONCLUSIONS: Based on promising initial findings, future acceptability and feasibility testing in a larger sample is warranted. Future versions might include push notifications to facilitate engagement in the additional app features.

Estradiol effects on an emotional interference task in adolescents with current and remitted depression.

Attentional biases to emotional stimuli are thought to reflect vulnerability for mood disorder onset and maintenance. This study examined the association between the endogenous sex hormone estradiol and emotional attentional biases in adolescent females with either current or remitted depression. Three groups of participants (mean age ± SD) completed the Emotional Interrupt Task: 1) 20 adolescent females (15.1 ± 1.83 years) currently diagnosed with Major Depressive Disorder (MDD), 2) 16 adolescent females (16.4 ± 1.31 years) who had experienced at least one episode of MDD in their lifetime but currently met criteria for MDD in remission, and 3) 30 adolescent female (15.4 ± 1.83 years) healthy controls. Attentional interference (AI) scores were calculated as differences in target response reaction time between trials with emotional facial expressions versus neutral facial expressions. Estradiol levels were assayed by Salimetrics LLC using saliva samples collected within 30 min of waking on assessment days. Robust multiple regression with product terms evaluated estradiol's main effect on AI scores, as well as hypothesized estradiol × diagnostic group interactions. Although neither mean estradiol levels nor mean AI scores in the current-MDD and remitted-MDD groups differed from controls, the relationship between estradiol and overall AI score differed between control adolescents and the remitted-MDD group. Specifically, the remitted-MDD adolescents performed worse (i.e., showed greater attentional interference) when they had higher estradiol; no significant relationship existed in the current-MDD group. Because this finding was driven by angry and not happy stimuli, it appears higher estradiol levels were associated with greater susceptibility to the attention-capturing effects of negatively-valenced emotional content in girls at risk for MDD from prior history.

The effect of ten versus twenty minutes of mindfulness meditation on state mindfulness and affect.

We aimed to elucidate the effects of "dose" of a single-session of mindfulness meditation on state mindfulness and affect as well as moderators of effects. 372 adults recruited remotely via Amazon's MTurk platform were randomly assigned to either a: 10-min mindfulness meditation, 20-min mindfulness meditation, 10-min control, or 20-min control. Control conditions were recordings of a National Geographic article. Primary outcomes were changes in state mindfulness, anxiety, and negative and positive affect. Moderator variables included neuroticism, trait mindfulness, and prior meditation experience. Collapsing across doses, participants in mindfulness conditions reported greater increases in state mindfulness than in control conditions. There was a greater increase in state mindfulness in the 10-min mindfulness condition versus 10-min control condition. There were no differences between 10- and 20-min mindfulness conditions. Exploratory moderation analyses indicated that meditation (10 or 20) versus control (10 or 20) predicted increased state mindfulness among participants with lower trait mindfulness. Additionally, 20-min versus 10-min meditation predicted greater decreases in state anxiety among individuals with high trait mindfulness. Dose-response relationships were minimal, suggesting that 10 and 20 min of meditation may improve state mindfulness comparably. Findings support the benefits of brief mindfulness meditation and suggest that trait mindfulness moderates certain outcomes.

Listening to the Data: Computational Approaches to Addiction and Learning.

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.