BACKGROUND:Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have a major impact. This study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. METHODS:Two datasets were studied: (A) one consisted of n = 54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n = 15 subjects treated for 8 weeks. Connectome predictive modeling (CPM) was used to predict treatment response, as assessed with the PARS. The main analysis included network edges positively correlated with treatment outcome and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses are also presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r, and mean absolute error (MAE). RESULTS:The main model showed a MAE of approximately 3.5 (95% CI: [3.1-3.8]) points, an R2 of 0.08 [-0.14-0.26], and an r of 0.38 [0.24-0.511]. When testing this model in the left-out sample (B), the results were similar, with an MAE of 3.4 [2.8-4.7], R2-0.65 [-2.29-0.16], and r of 0.4 [0.24-0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2. CONCLUSIONS:The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, this study does not support the extensive use of CPM to predict outcomes in pediatric anxiety.

Objective:To develop and evaluate an automated classification system for labeling Exposure Process Coding System (EPCS) quality codes-specifically exposure and encourage events-during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques. Materials and Methods:The system was trained and tested on 360 manually labeled pediatric Obsessive-Compulsive Disorder (OCD) therapy sessions from 3 clinical trials. Audio recordings were transcribed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Transcription accuracy was evaluated via word error rate (WER) on manual transcriptions of 2-minute audio segments compared against ASR-generated transcripts. The resulting text was analyzed with transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), Sentence-BERT, and Meta Llama 3. Models were trained to predict EPCS codes in 2 classification settings: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model. Results:With respect to transcription accuracy, Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs 0.51). For sequence classification setting, Llama 3 models achieved high performance with area under the ROC curve (AUC) scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events. Discussion and Conclusion:Current ASR and transformer-based models enable automated quality coding of in-person exposure therapy sessions. These findings demonstrate potential for real-time assessment in clinical practice and scalable research on effective therapy methods. Future work should focus on optimization, including improvements in ASR accuracy, expanding training datasets, and multimodal data integration.

BACKGROUND:The macro-social and environmental conditions in which people live, such as the level of a country's development or inequality, are associated with brain-related disorders. However, the relationship between these systemic environmental factors and the brain remains unclear. We aimed to determine the association between the level of development and inequality of a country and the brain structure of healthy adults. METHODS:We conducted a cross-sectional study pooling brain imaging (T1-based) data from 145 magnetic resonance imaging (MRI) studies in 7,962 healthy adults (4,110 women) in 29 different countries. We used a meta-regression approach to relate the brain structure to the country's level of development and inequality. RESULTS:Higher human development was consistently associated with larger hippocampi and more expanded global cortical surface area, particularly in frontal areas. Increased inequality was most consistently associated with smaller hippocampal volume and thinner cortical thickness across the brain. CONCLUSIONS:Our results suggest that the macro-economic conditions of a country are reflected in its inhabitants' brains and may explain the different incidence of brain disorders across the world. The observed variability of brain structure in health across countries should be considered when developing tools in the field of personalized or precision medicine that are intended to be used across the world.

Movie-watching fMRI has emerged as a theoretically viable platform for studying neurobiological substrates of affective states and emotional disorders such as pathological anxiety. However, using anxiety-inducing movie clips to probe relevant states impacted by psychopathology could risk exacerbating in-scanner movement, decreasing signal quality/quantity and thus statistical power. This could be especially problematic in target populations such as children who typically move more in the scanner. Consequently, we assessed: (1) the extent to which an anxiety-inducing movie clip altered in-scanner data quality (movement, censoring, and DVARS) in a pediatric sample with and without anxiety disorders (n = 78); and (2) investigated interactions between anxiety symptoms and movie-attenuated motion in a highly powered, transdiagnostic pediatric sample (n = 2058). Our results suggest anxiogenic movie-watching in fact reduces in-scanner movement compared to resting-state, increasing the quantity/quality of data. In one measure, pathological anxiety appeared to impact movie-attenuated motion, but the effect was small. Given potential boosts to data quality, future developmental neuroimaging studies of anxiety may benefit from the use of movie paradigms.

Importance:Given the expiration of expanded unemployment and other benefits during the COVID-19 pandemic, it is important to understand the association between pandemic income or job loss and long-term implications on mental health. Objective:To evaluate the association between income or job loss due to the COVID-19 pandemic and later psychological distress. Design, Setting, and Participants:This cohort study used 5 waves of nationally representative, longitudinal survey data (September 16, 2019, through September 18, 2022) from the Pew Research Center's American Trends Panel. Doubly robust propensity score-weighted quasi-Poisson models were used to estimate the association of self or household income or job loss during the early phase of the COVID-19 pandemic with later psychological distress, while controlling for pre-income loss characteristics (demographics, finances, and psychological distress). The study sample comprised US working-age adults (aged 18-64 years) who had not experienced income or job loss due to the COVID-19 pandemic by March 24, 2020. Exposure:Participant-reported self or household income or job loss (ie, reduced hours or demand for work) due to the COVID19 pandemic between March 24, 2020, and August 16, 2020. Main Outcome and Measures:Psychological distress was measured using a composite scale of 0 to 15 based on participants' reported frequency of feeling depressed, on edge, sleepless, lonely, and hopeless in the past week in March 2020, February 2021, and September 2022. Results:Of 1392 working-age adults (survey weighted 52.7% male and 47.7% aged 30-49 years) who had not reported income or job loss before March 24, 2020, a survey weighted 35.7% reported job or income loss between March 24 and August 16, 2020. Early-phase pandemic income or job loss was associated with higher distress in February 2021 (estimated ratio, 1.09; 95% CI, 1.01-1.18; P = .03) and September 2022 (estimated ratio, 1.11; 95% CI, 1.02-1.22; P = .02) among participants who experienced job or income loss between March 24 and August 16, 2020, compared with the propensity score-weighted mean in the group who did not experience income loss. Conclusions and Relevance:These small but significant within-person associations between early-phase pandemic household income or job loss and psychological distress up to 29 months later suggest that policies are needed to support people with income or job loss to help mitigate the long-term adverse mental health outcomes of economic disruption.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

While the association between assets and depression has been established, less is known about the link between financial strain and depression. Given rising financial strain and economic inequity due to the COVID-19 pandemic, understanding the role that financial strain plays in shaping population depression in the United States is particularly salient. We conducted a scoping review of the peer-reviewed literature on financial strain and depression published from inception through January 19, 2023, in Embase, Medline via PubMed, and PsycINFO, PsycArticles, SocINDEX, and EconLit via Ebsco. We searched, reviewed, and synthesized the literature on longitudinal studies on financial strain and depression conducted in the United States. Four thousand and four unique citations were screened for eligibility. Fifty-eight longitudinal, quantitative articles on adults in the United States were included in the review. Eighty-three percent of articles ( n = 48) reported a significant, positive association between financial strain and depression. Eight articles reported mixed results, featuring non-significant associations for some sub-groups and significant associations for others, one article was unclear, and one article reported no significant association between financial strain and depression. Five articles featured interventions to reduce depressive symptoms. Effective interventions included coping mechanisms to improve one’s financial situation (e.g., mechanisms to assist in finding employment), to modify cognitive behavior (e.g., reframing mindset), and to engage support (e.g., engaging social and community support). Successful interventions were tailored to participants, were group-based (e.g., they included family members or other job seekers), and occurred over multiple sessions. While depression was defined consistently, financial strain was defined variably. Gaps in the literature included studies featuring Asian populations in the United States and interventions to reduce financial strain. There is a consistent, positive association between financial strain and depression in the United States. More research is needed to identify and test interventions that mitigate the ill effects of financial strain on population’s mental health.

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women's worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women's brains and provide initial evidence for neuroscience-informed policies for gender equality.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Numerous climate models display large-amplitude, long-period variability associated with quasiperiodic convection in the Southern Ocean, but the mechanisms responsible for producing such oscillatory convection are poorly understood. In this paper we identify three feedbacks that help generate such oscillations within an Earth system model with a particularly regular oscillation. The first feedback involves increased (decreased) upward mixing of warm interior water to the surface, resulting in more (less) evaporation and loss of heat to the atmosphere which produces more (less) mixing. This positive feedback helps explain why temperature anomalies are not damped out by surface forcing. A second key mechanism involves convective (nonconvective) events in the Weddell Sea causing a relaxation (intensification) of westerly winds, which at some later time results in a pattern of currents that reduces (increases) the advection of freshwater out of the Weddell Sea. This allows for the surface to become lighter (denser) which in turn can dampen (trigger) convection-so that the overall feedback is a negative one with a delay-helping to produce a multidecadal oscillation time scale. The decrease (increase) in winds associated with convective (nonconvective) states also results in a decrease (increase) in the upward mixing of salt in the Eastern Weddell Sea, creating a negative (positive) salinity anomaly that propagates into the Western Weddell Sea and dampens (triggers) convection-again producing a negative feedback with a delay. A principal oscillatory pattern analysis yields a reasonable prediction for the period of oscillation. Strengths of the feedbacks are sensitive to parameterization of mesoscale eddies.