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This review has two primary objectives: (1) to offer a balanced examination of recent findings on the relationship between screen media activity (SMA) in young individuals and outcomes such as sleep patterns, mood disturbances, anxiety-related concerns, and cognitive processes; and (2) to introduce a novel multi-level system model that integrates these findings, resolves contradictions in the literature, and guides future studies in examining key covariates affecting the SMA-mental health relationship. Key findings include: (1) Several meta-analyses reveal a significant association between SMA and mental health issues, particularly anxiety and depression, including specific negative effects linked to prolonged screen time; (2) substantial evidence indicates that SMA has both immediate and long-term impacts on sleep duration and quality; (3) the relationship between SMA and cognitive functioning is complex, with mixed findings showing both positive and negative associations; and (4) the multifaceted relationship between SMA and various aspects of adolescent life is influenced by a wide range of environmental and contextual factors. SMA in youth is best understood within a complex system encompassing individual, caregiver, school, peer, and environmental factors, as framed by Bronfenbrenner's ecological systems theory, which identifies five interrelated systems (microsystem, mesosystem, exosystem, macrosystem, and chronosystem) that influence development across both proximal and distal levels of the environment. This model provides a framework for future research to examine these interactions, considering moderating factors, and to develop targeted interventions that can mitigate potential adverse effects of SMA on mental well-being.

BackgroundAn inflammation-induced imbalance in the kynurenine pathway (KP) has been reported in major depressive disorder but the utility of these metabolites as predictive or therapeutic biomarkers of behavioral activation (BA) therapy is unknown.MethodsSerum samples were provided by 56 depressed individuals before BA therapy and 29 of these individuals also provided samples after 10 weeks of therapy to measure cytokines and KP metabolites. The PROMIS Depression Scale (PROMIS-D) and the Sheehan Disability Scale were administered weekly and the Beck depression inventory was administered pre- and post-therapy. Data were analyzed with linear mixed-effect, general linear, and logistic regression models. The primary outcome for the biomarker analyses was the ratio of kynurenic acid to quinolinic acid (KynA/QA).ResultsBA decreased depression and disability scores (p's < 0.001, Cohen's d's > 0.5). KynA/QA significantly increased at post-therapy relative to baseline (p < 0.001, d = 2.2), an effect driven by a decrease in QA post-therapy (p < 0.001, uncorrected, d = 3.39). A trend towards a decrease in the ratio of kynurenine to tryptophan (KYN/TRP) was also observed (p = 0.054, uncorrected, d = 0.78). Neither the change in KynA/QA, nor baseline KynA/QA were associated with response to BA therapy.ConclusionThe current findings together with previous research show that electronconvulsive therapy, escitalopram, and ketamine decrease concentrations of the neurotoxin, QA, raise the possibility that a common therapeutic mechanism underlies diverse forms of anti-depressant treatment but future controlled studies are needed to test this hypothesis.

This study examined how resting-state functional magnetic resonance imaging (rs-fMRI) data quality and availability relate to clinical and sociodemographic variables within the Adolescent Brain Cognitive Development Study. A sample of participants with an adequate sample of quality baseline rs-fMRI data containing low average motion (framewise displacement ≤ 0.15; low-noise; n = 4,356) was compared to a sample of participants without an adequate sample of quality data and/or containing high average motion (higher-noise; n = 7,437) using Chi-squared analyses and t-tests. A linear mixed model examined relationships between clinical and sociodemographic characteristics and average head motion in the sample with low-noise data. Relative to the sample with higher-noise data, the low-noise sample included more females, youth identified by parents as non-Hispanic white, and youth with married parents, higher parent education, and greater household incomes (ORs = 1.32-1.42). Youth in the low-noise sample were also older and had higher neurocognitive skills, lower BMIs, and fewer externalizing and neurodevelopmental problems (ds = 0.12-0.30). Within the low-noise sample, several clinical and demographic characteristics related to motion. Thus, participants with low-noise rs-fMRI data may be less representative of the general population and motion may remain a confound in this sample. Future rs-fMRI studies of youth should consider these limitations in the design and analysis stages in order to optimize the representativeness and clinical relevance of analyses and results.

The use of less effective emotional regulation strategies, such as distraction, may relate to greater reliance on avoidance behavior under conflict. Tulsa 1000 participants (n = 478) were recruited trans diagnostically and completed an approach avoidance conflict (AAC) task. Following the task, participants reported their use of distraction as an emotional regulation strategy. Quantile regression showed negative main effects of distraction and age on approach behavior under conflict. This suggests that individuals who use distraction as a regulation strategy are more likely to engage in avoidance behavior under conflict, which has treatment implications., Author(s): Ebony A Walker [sup.1] , Robin L Aupperle [sup.1] [sup.2] , Martin P Paulus [sup.1] [sup.2] , Maria Ironside [sup.1] Author Affiliations: (1) grid.417423.7, 0000 0004 0512 8863, Laureate [...]

Objective: To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students. Methods: 356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores. Results: 9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95% CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk. Conclusion: Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.

BACKGROUND:Only 40-60% of patients with generalized anxiety disorder experience long-lasting improvement with gold standard psychosocial interventions. Identifying neurobehavioral factors that predict treatment success might provide specific targets for more individualized interventions, fostering more optimal outcomes and bringing us closer to the goal of "personalized medicine." Research suggests that reward and threat processing (approach/avoidance behavior) and cognitive control may be important for understanding anxiety and comorbid depressive disorders and may have relevance to treatment outcomes. This study was designed to determine whether approach-avoidance behaviors and associated neural responses moderate treatment response to exposure-based versus behavioral activation therapy for generalized anxiety disorder. METHODS/DESIGN:We are conducting a randomized controlled trial involving two 10-week group-based interventions: exposure-based therapy or behavioral activation therapy. These interventions focus on specific and unique aspects of threat and reward processing, respectively. Prior to and after treatment, participants are interviewed and undergo behavioral, biomarker, and neuroimaging assessments, with a focus on approach and avoidance processing and decision-making. Primary analyses will use mixed models to examine whether hypothesized approach, avoidance, and conflict arbitration behaviors and associated neural responses at baseline moderate symptom change with treatment, as assessed using the Generalized Anxiety Disorder-7 item scale. Exploratory analyses will examine additional potential treatment moderators and use data reduction and machine learning methods. DISCUSSION:This protocol provides a framework for how studies may be designed to move the field toward neuroscience-informed and personalized psychosocial treatments. The results of this trial will have implications for approach-avoidance processing in generalized anxiety disorder, relationships between levels of analysis (i.e., behavioral, neural), and predictors of behavioral therapy outcome. TRIAL REGISTRATION:The study was retrospectively registered within 21 days of first participant enrollment in accordance with FDAAA 801 with ClinicalTrials.gov, NCT02807480. Registered on June 21, 2016, before results.

The authors regret that the article was published with the incorrect grant number listed for the funding source. The correct Centers of Biomedical Research Excellence grant number is P20GM109097 (PD: Jennifer Hays-Grudo; funded by the National Institutes of Health). The authors apologize for any inconvenience caused.

Background: We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach-avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC. Methods: We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures. Results: Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (p = 0.008, d = 0.31) regardless of valence, and weaker striatal engagement during reward feedback (p = 0.046, d = -0.27) regardless of the presence of monetary reward. They also had blunted amygdala activity during decision-making (p = 0.023, d = -0.32) regardless of the presence of conflict. Across groups, approach behaviour during conflict decision-making was inversely correlated with striatal activation during affective stimuli (p < 0.001, r = -0.28) and positively related to striatal activation during reward feedback (p < 0.001, r = 0.27). Limitations: Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference. Conclusion: Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach., Introduction Approach-avoidance conflict (AAC) occurs when a person makes a behavioural choice that might have rewarding or threatening outcomes. (1-3) Neural and behavioural mechanisms during AAC decision-making are relevant across [...]

How parents manifest symptoms of anxiety or depression may affect how children learn to modulate their own distress, thereby influencing the children's risk for developing an anxiety or mood disorder. Conversely, children's mental health symptoms may impact parents' experiences of negative emotions. Therefore, mental health symptoms can have bidirectional effects in parent-child relationships, particularly during moments of distress or frustration (e.g., when a parent or child makes a costly mistake). The present study used simultaneous functional magnetic resonance imaging (fMRI) of parent-adolescent dyads to examine how brain activity when responding to each other's costly errors (i.e., dyadic error processing) may be associated with symptoms of anxiety and depression. While undergoing simultaneous fMRI scans, healthy dyads completed a task involving feigned errors that indicated their family member made a costly mistake. Inter-brain, random-effects multivariate modeling revealed that parents who exhibited decreased medial prefrontal cortex and posterior cingulate cortex activation when viewing their child's costly error response had children with more symptoms of depression and anxiety. Adolescents with increased anterior insula activation when viewing a costly error made by their parent had more anxious parents. These results reveal cross-brain associations between mental health symptomatology and brain activity during parent-child dyadic error processing.

This study used a machine learning framework in conjunction with a large battery of measures from 9,718 school-age children (ages 9-11) from the Adolescent Brain Cognitive Development[superscript SM] (ABCD) Study to identify factors associated with fluid cognitive functioning (FCF), or the capacity to learn, solve problems, and adapt to novel situations. The identified algorithm explained 14.74% of the variance in FCF, replicating previously reported socioeconomic and mental health contributors to FCF, and adding novel and potentially modifiable contributors, including extracurricular involvement, screen media activity, and sleep duration. Pragmatic interventions targeting these contributors may enhance cognitive performance and protect against their negative impact on FCF in children.

Introduction: In the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples. Methods: The current study used machine learning (ML) to examine other nondemographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women's Risk/Needs Assessment (WRNA). Results: ML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p < 0.05; ds = 0.29 to 0.81). Discussion: Results point towards the complexity involved in attempting to predict treatment completion at the individual level but also provide potential targets to inform future research aiming to reduce recidivism. [ABSTRACT FROM AUTHOR]

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

Post-traumatic stress disorder (PTSD) is classified as a traumatic stress-related condition and is most often discussed in terms of emotional dysfunction. However, given that cognitive and emotional processes are intricately intertwined, implemented by overlapping brain networks, and effectively integrated in at least some of the same regions (e.g., prefrontal cortex, for a review, see Crocker et al. 2013), an abundance of literature now highlights the key role that cognitive functioning plays in both the development and maintenance (or exacerbation) of PTSD symptoms (Aupperle et al. 2012a; Verfaellie et al. 2012). Findings from this body of work detail objective impairment in neuropsychological function in those with PTSD (Brandes et al. 2002; Hayes et al. 2012a; Koenen et al. 2001). Yet despite the impact of neurocognition on PTSD treatment engagement and success (e.g., Haaland et al. 2016; Nijdam et al. 2015) and conversely, the role of PTSD treatment in normalizing cognitive dysfunction, a much smaller literature exists on neurocognitive changes following treatment for PTSD. Even aside from its role in treatment, cognitive functioning in PTSD has significant implications for daily functioning for individuals with this disorder, as cognition is predictive of school achievement, obtaining and maintaining employment, job advancement, maintaining relationships, greater wealth, and better health and quality of life (e.g., Diamond and Ling 2016).

Post-traumatic stress disorder (PTSD) is classified as a traumatic stress-related condition and is most often discussed in terms of emotional dysfunction. However, given that cognitive and emotional processes are intricately intertwined, implemented by overlapping brain networks, and effectively integrated in at least some of the same regions (e.g., prefrontal cortex, for a review, see Crocker et al. 2013), an abundance of literature now highlights the key role that cognitive functioning plays in both the development and maintenance (or exacerbation) of PTSD symptoms (Aupperle et al. 2012a; Verfaellie et al. 2012). Findings from this body of work detail objective impairment in neuropsychological function in those with PTSD (Brandes et al. 2002; Hayes et al. 2012a; Koenen et al. 2001). Yet despite the impact of neurocognition on PTSD treatment engagement and success (e.g., Haaland et al. 2016; Nijdam et al. 2015) and conversely, the role of PTSD treatment in normalizing cognitive dysfunction, a much smaller literature exists on neurocognitive changes following treatment for PTSD. Even aside from its role in treatment, cognitive functioning in PTSD has significant implications for daily functioning for individuals with this disorder, as cognition is predictive of school achievement, obtaining and maintaining employment, job advancement, maintaining relationships, greater wealth, and better health and quality of life (e.g., Diamond and Ling 2016).