Cortical and Subcortical Brain Alterations in Specific Phobia and Its Animal and Blood-Injection-Injury Subtypes: A Mega-Analysis From the ENIGMA Anxiety Working Group.

OBJECTIVE: Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults). METHODS: Data sets from 31 original studies were combined to create a final sample with 1,452 participants with phobia and 2,991 healthy participants (62.7% female; ages 5-90). Imaging processing and quality control were performed using established ENIGMA protocols. Subcortical volumes as well as cortical surface area and thickness were examined in a preregistered analysis. RESULTS: Compared with the healthy control group, the phobia group showed mostly smaller subcortical volumes, mixed surface differences, and larger cortical thickness across a substantial number of regions. The phobia subgroups also showed differences, including, as hypothesized, larger medial orbitofrontal cortex thickness in BII phobia (N=182) compared with animal phobia (N=739). All findings were driven by adult participants; no significant results were observed in children and adolescents. CONCLUSIONS: Brain alterations associated with specific phobia exceeded those of other anxiety disorders in comparable analyses in extent and effect size and were not limited to reductions in brain structure. Moreover, phenomenological differences between phobia subgroups were reflected in diverging neural underpinnings, including brain areas related to fear processing and higher cognitive processes. The findings implicate brain structure alterations in specific phobia, although subcortical alterations in particular may also relate to broader internalizing psychopathology.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders.

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing.

Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 (p < 0.01) on the previously unseen 3942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment.

BACKGROUND: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. METHODS: Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. RESULTS: Best performances were reached by our models involving early treatment characteristics (recall: 0.75-0.76; AUC: 0.71-0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. CONCLUSIONS: Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms.

Introduction: Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has. Methods: We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals. Results: Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention. Discussion: We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed. Systematic Review Registration: Identifier: CRD42022357408.

Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Impact of COVID-19 pandemic related stressors on patients with anxiety disorders: A cross-sectional study.

The COVID-19 pandemic and related containment measures are affecting mental health, especially among patients with pre-existing mental disorders. The aim of this study was to investigate the effect of the first wave and its aftermath of the pandemic in Germany (March-July) on psychopathology of patients diagnosed with panic disorder, social anxiety disorder and specific phobia who were on the waiting list or in current treatment at a German university-based outpatient clinic. From 108 patients contacted, forty-nine patients (45.37%) completed a retrospective survey on COVID-19 related stressors, depression, and changes in anxiety symptoms. Patients in the final sample (n = 47) reported a mild depression and significant increase in unspecific anxiety (d = .41), panic symptoms (d = .85) and specific phobia (d = .38), while social anxiety remained unaltered. Pandemic related stressors like job insecurities, familial stress and working in the health sector were significantly associated with more severe depression and increases in anxiety symptoms. High pre-pandemic symptom severity (anxiety/depression) was a risk factor, whereas meaningful work and being divorced/separated were protective factors (explained variance: 46.5% of changes in anxiety and 75.8% in depressive symptoms). In line with diathesis-stress models, patients show a positive association between stressors and symptom load. Health care systems are requested to address the needs of this vulnerable risk group by implementing timely and low-threshold interventions to prevent patients from further deterioration.

Who is seeking help for psychological distress associated with the COVID-19 pandemic? Characterization of risk factors in 1269 participants accessing low-threshold psychological help.

BACKGROUND: The COVID-19 pandemic and accompanying restrictions are associated with substantial psychological distress. However, it is unclear how this increased strain translates into help-seeking behavior. Here, we aim to characterize those individuals who seek help for COVID-19 related psychological distress, and examine which factors are associated with their levels of distress in order to better characterize vulnerable groups. METHODS: We report data from 1269 help-seeking participants subscribing to a stepped-care program targeted at mental health problems due to the COVID-19 pandemic. Sample characteristics were compared to population data, and linear regression analyses were used to examine which risk factors and stressors were associated with current symptom levels. RESULTS: Seeking for help for COVID-19 related psychological distress was characterized by female gender, younger age, and better education compared to the general population. The majority reported mental health problems already before the pandemic. 74.5% of this help-seeking sample also exceeded clinical thresholds for depression, anxiety, or somatization. Higher individual symptom levels were associated with higher overall levels of pandemic stress, younger age, and pre-existing mental health problems, but were buffered by functional emotion regulation strategies. CONCLUSIONS: Results suggest a considerable increase in demand for mental-healthcare in the pandemic aftermath. Comparisons with the general population indicate diverging patterns in help-seeking behavior: while some individuals seek help themselves, others should be addressed directly. Individuals that are young, have pre-existing mental health problems and experience a high level of pandemic stress are particularly at-risk for considerable symptom load. Mental-healthcare providers should use these results to prepare for the significant increase in demand during the broader aftermath of the COVID-19 pandemic as well as allocate limited resources more effectively.

Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.

ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders.

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.

Behavioral and Magnetoencephalographic Correlates of Fear Generalization Are Associated With Responses to Later Virtual Reality Exposure Therapy in Spider Phobia.

BACKGROUND: Because overgeneralization of fear is a pathogenic marker of anxiety disorders, we investigated whether pretreatment levels of fear generalization in spider-phobic patients are related to their response to exposure-based treatment to identify pretreatment moderators of treatment success. METHODS: A total of 90 patients with spider phobia completed pretreatment clinical and magnetoencephalography assessments, one session of virtual reality exposure therapy, and a posttreatment clinical assessment. Based on the primary outcome (30% symptom reduction in self-reported symptoms), they were categorized as responders or nonresponders. In a pretreatment magnetoencephalography fear generalization paradigm involving fear conditioning with 2 unconditioned stimuli (UCS), we obtained fear ratings, UCS expectancy ratings, and event-related fields to conditioned stimuli (CS: CS-, CS+) and 7 different generalization stimuli on a perceptual continuum from CS- to CS+. RESULTS: Before treatment, nonresponders showed behavioral overgeneralization indicated by more linear generalization gradients in fear ratings. Analyses of magnetoencephalography source estimations revealed that nonresponders showed a decline of their (inhibitory) frontal activations to safety-signaling CS- and generalization stimuli compared with CS+ over time, while responders maintained these activations at early (<300 ms) and late processing stages. CONCLUSIONS: Results provide initial evidence that pretreatment differences of behavioral and neural markers of fear generalization may act as moderators of later responses to behavioral exposure. Stimulating further research on fear generalization as a potential predictive marker, our findings are an important first step in the attempt to identify patients who may not benefit from exposure therapy and to personalize and optimize treatment strategies for this vulnerable patient group.

Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach.

OBJECTIVE: Predicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-based predictions within digital mental health interventions. Therefore, this study evaluates the performance of machine learning in predicting treatment response in a digital mental health intervention designed for treating depression and anxiety. METHODS: Several algorithms were trained based on the data of 970 participants to predict a significant reduction in depression and anxiety symptoms using clinical and sociodemographic variables. As a random forest classifier performed best over cross-validation, it was used to predict the outcomes of 279 new participants. RESULTS: The random forest achieved an accuracy of 0.71 for the test set (base rate: 0.67, area under curve (AUC): 0.60, p = 0.001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their Patient Health Questionnaire-9 (PHQ-9) (-2.7, p = 0.004) and General Anxiety Disorder Screener-7 values (-3.7, p < 0.001) compared to responders. Besides pre-treatment Patient Health Questionnaire-9 and General Anxiety Disorder Screener-7 values, the self-reported motivation, type of referral into the programme (self vs. healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire items contributed most to the predictions. CONCLUSIONS: This study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.

Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group.

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.

Mental health trajectories of individuals and families following the COVID-19 pandemic: Study protocol of a longitudinal investigation and prevention program.

INTRODUCTION: Many adults, adolescents and children are suffering from persistent stress symptoms in the face of the COVID-19 pandemic. This study aims to characterize long-term trajectories of mental health and to reduce the transition to manifest mental disorders by means of a stepped care program for indicated prevention. METHODS AND ANALYSIS: Using a prospective-longitudinal design, we will assess the mental strain of the pandemic using the Patient Health Questionnaire, Strength and Difficulties Questionnaire and Spence Child Anxiety Scale. Hair samples will be collected to assess cortisol as a biological stress marker of the previous months. Additionally, we will implement a stepped-care program with online- and face-to-face-interventions for adults, adolescents, and children. After that we will assess long-term trajectories of mental health at 6, 12, and 24 months follow-up. The primary outcome will be psychological distress (depression, anxiety and somatoform symptoms). Data will be analyzed with general linear model and machine learning. This study will contribute to the understanding of the impact of the COVID-19 pandemic on mental health. The evaluation of the stepped-care program and longitudinal investigation will inform clinicians and mental health stakeholders on populations at risk, disease trajectories and the sufficiency of indicated prevention to ameliorate the mental strain of the pandemic. ETHICS AND DISSEMINATION: The study is performed according to the Declaration of Helsinki and was approved by the Ethics Committee of the Department of Psychology at the Humboldt Universität zu Berlin (no. 2020-35). TRIAL REGISTRATION NUMBER: DRKS00023220.

Clinical predictors of treatment response towards exposure therapy in virtuo in spider phobia: A machine learning and external cross-validation approach.

While being highly effective on average, exposure-based treatments are not equally effective in all patients. The a priori identification of patients with a poor prognosis may enable the application of more personalized psychotherapeutic interventions. We aimed at identifying sociodemographic and clinical pre-treatment predictors for treatment response in spider phobia (SP). N = 174 patients with SP underwent a highly standardized virtual reality exposure therapy (VRET) at two independent sites. Analyses on group-level were used to test the efficacy. We applied a state-of-the-art machine learning protocol (Random Forests) to evaluate the predictive utility of clinical and sociodemographic predictors for a priori identification of individual treatment response assessed directly after treatment and at 6-month follow-up. The reliability and generalizability of predictive models was tested via external cross-validation. Our study shows that one session of VRET is highly effective on a group-level and is among the first to reveal long-term stability of this treatment effect. Individual short-term symptom reductions could be predicted above chance, but accuracies dropped to non-significance in our between-site prediction and for predictions of long-term outcomes. With performance metrics hardly exceeding chance level and the lack of generalizability in the employed between-site replication approach, our study suggests limited clinical utility of clinical and sociodemographic predictors. Predictive models including multimodal predictors may be more promising.

Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: A voxel-based morphometry meta-analysis.

The high comorbidity of Major Depressive Disorder (MDD), Anxiety Disorders (ANX), and Posttraumatic Stress Disorder (PTSD) has hindered the study of their structural neural correlates. The authors analyzed specific and common grey matter volume (GMV) characteristics by comparing them with healthy controls (HC). The meta-analysis of voxel-based morphometry (VBM) studies showed unique GMV diminutions for each disorder (p < 0.05, corrected) and less robust smaller GMV across diagnostics (p < 0.01, uncorrected). Pairwise comparison between the disorders showed GMV differences in MDD versus ANX and in ANX versus PTSD. These results endorse the hypothesis that unique clinical features characterizing MDD, ANX, and PTSD are also reflected by disorder specific GMV correlates.

Psychological Predictors of Cognitive-Behavioral Therapy Outcomes for Anxiety and Depressive Disorders in Children and Adolescents: A Systematic Review and Meta-Analysis.

BACKGROUND: By understanding specific differences between responders to a treatment and non-responders, treatment modalities may be fitted to the individual in order to increase effectiveness, a concept known as "precision medicine". This systematic review and meta-analysis investigated which pretreatment patient and family characteristics may predict the outcome of cognitive-behavioral therapy (CBT) in clinically anxious and/or depressed youth. In particular, higher symptom severity, more severe co-occurring anxiety or depression and more severe parental psychopathology were hypothesized to predict a worse CBT outcome. METHODS: The databases PubMed, PsycINFO and Cochrane Library were searched; 73 publications were included in the review from which 23 studies were used for the meta-analysis. RESULTS: Higher symptom severity represented a clinically relevant predictor of a worse CBT outcome, with large effects estimated by meta-analysis. Further, parental psychopathology was significant and detrimental for CBT outcome in anxious but not depressed youth, while the effects for co-occurring anxiety and depression remained unclear. The additional results of the review show that only few characteristics seemed to be clearly associated with a worse CBT outcome, namely worse coping skills and, restricted to depressed patients, more non-suicidal self-injury. LIMITATIONS: The available evidence was of only moderate quality in general, further high-quality research with more transparent reporting is needed. CONCLUSIONS: The patient characteristics identified as being relevant for CBT outcome may represent important candidates for use in single patient prediction models for precision medicine in the field of child and adolescent psychotherapy. The review was preregistered on PROSPERO (ID: CRD42018116881).

Identifying CBT non-response among OCD outpatients: A machine-learning approach.

Objectives: Machine learning models predicting treatment outcomes for individual patients may yield high clinical utility. However, few studies tested the utility of easy to acquire and low-cost sociodemographic and clinical data. In previous work, we reported significant predictions still insufficient for immediate clinical use in a sample with broad diagnostic spectrum. We here examined whether predictions will improve in a diagnostically more homogeneous yet large and naturalistic obsessive-compulsive disorder (OCD) sample. Methods: We used sociodemographic and clinical data routinely acquired during CBT treatment of n = 533 OCD subjects in a specialized outpatient clinic. Results: Remission was predicted with 65% (p = 0.001) balanced accuracy on unseen data for the best model. Higher OCD symptom severity predicted non-remission, while higher age of onset of first OCD symptoms and higher socioeconomic status predicted remission. For dimensional change, prediction achieved r = 0.31 (p = 0.001) between predicted and actual values. Conclusions: The comparison with our previous work suggests that predictions within a diagnostically homogeneous sample, here OCD, are not per se superior to a more diverse sample including several diagnostic groups. Using refined psychological predictors associated with disorder etiology and maintenance or adding further data modalities as neuroimaging or ecological momentary assessments are promising in order to further increase prediction accuracy.

The modulating impact of cigarette smoking on brain structure in panic disorder: a voxel-based morphometry study.

Cigarette smoking increases the likelihood of developing anxiety disorders, among them panic disorder (PD). While brain structures altered by smoking partly overlap with morphological changes identified in PD, the modulating impact of smoking as a potential confounder on structural alterations in PD has not yet been addressed. In total, 143 PD patients (71 smokers) and 178 healthy controls (62 smokers) participated in a multicenter magnetic resonance imaging (MRI) study. T1-weighted images were used to examine brain structural alterations using voxel-based morphometry in a priori defined regions of the defensive system network. PD was associated with gray matter volume reductions in the amygdala and hippocampus. This difference was driven by non-smokers and absent in smoking subjects. Bilateral amygdala volumes were reduced with increasing health burden (neither PD nor smoking > either PD or smoking > both PD and smoking). As smoking can narrow or diminish commonly observed structural abnormalities in PD, the effect of smoking should be considered in MRI studies focusing on patients with pathological forms of fear and anxiety. Future studies are needed to determine if smoking may increase the risk for subsequent psychopathology via brain functional or structural alterations.