Background Behavioral emotion regulation (ER) is conceptually distinct from cognitive ER, yet dedicated instruments to assess behavioral strategies are limited and rarely validated in clinical samples. Both ER domains relate to of the psychopathology of internalizing disorders, with potentially different contributions across diagnoses. Aims This study evaluates the psychometric properties of the German version of the Behavioral Emotion Regulation Questionnaire (BERQ), including its factorial structure, reliability, construct validity, and measurement invariance across gender and three diagnostic groups: unipolar depression, anxiety disorders, and obsessive-compulsive disorder. Methods Baseline data from N = 462 outpatients with internalizing disorders prior to cognitive-behavioral therapy (CBT) were analyzed. Confirmatory factor analyses with subsample cross-validation, as well as measurement invariance testing and correlational and regression analyses, were conducted to evaluate the BERQ and its associations with clinical measures and other ER measures. Results The five-factor structure of the BERQ was supported, with good internal consistency and measurement invariance across gender and diagnostic groups. Several behavioral strategies were significantly associated with depressive symptoms and global symptom severity, with withdrawal showing the most pronounced effects. Behavioral ER explained additional variance in symptom severity beyond cognitive ER strategies. Latent mean differences indicated clinically relevant differences across diagnostic groups. Conclusions The findings support the German BERQ as a reliable and valid instrument for assessing behavioral ER in clinical populations and highlight the relevance of behavioral strategies for understanding the psychopathology of internalizing disorders. Improved measurement of behavioral ER may therefore contribute to advances in targeted interventions and clinical research.

Negative affect (NA), encompassing heightened states of sadness, anxiety, and guilt, is a key symptom across a spectrum of internalizing disorders. Recent advancements in digital phenotyping (DP) and machine learning (ML) may enable the automatic detection of short-term fluctuations in NA through digital phenotyping, a prerequisite for Just-In-Time Adaptive Interventions (JITAI). Evidence on the prediction of momentary NA with DP is sparse, but it indicates that personalized ML models are required to account for individual heterogeneities.This preregistered study is the first to analyze data from the PREACT-digital project, encompassing 242 outpatient subjects diagnosed with internalizing disorders. We examined whether passive sensor data (heart rate, steps, mobility, physical activity) could predict momentary NA, as assessed via ecologically momentary assessments (EMA). Personalized and population-based ML approaches were trained on 19,792 pairs of DP data and NA ratings. We found that personalized ML approaches substantially outperformed population-based models. The best model, however, only marginally exceeded the benchmark, predicting per-person mean NA. Our findings emphasize the need for personalized ML in DP studies. Future efforts could incorporate richer or more raw data streams or test sequential modelling approaches to help clarify whether DP and personalized ML could reliably inform just-in-time, data-driven support for individuals affected by internalizing disorders.

Abstract Elucidating the neurobiological basis of neurodevelopmental and psychiatric conditions (NDPCs) remains challenging because brain alterations vary within diagnoses and overlap across them. Whether diverse alterations follow a systematic organization that may reflect shared vulnerabilities remains unknown. Here, we assembled 10,135 individuals with schizophrenia, autism, bipolar, obsessive-compulsive, generalized anxiety, and major depressive disorders, and 11,998 reference participants across six continents through the ENIGMA consortium. Using normative modeling, we quantified individual deviations in cortical thickness, surface area, and subcortical volumes relative to lifespan reference trajectories (5 to 80 years). We show that structural deviations converged along cortical axes reflecting connectome organization, maturation, and cytoarchitectonic diversity. These axes mirrored typical population variation, but their expression differed across diagnoses and partly scaled with symptom severity. Even rare and highly individualized extreme deviations followed this organization, concentrating in densely connected regions. Finally, brain structural deviations overlapped substantially across diagnoses, while differences between them increased toward the association cortex. Together, we provide large-scale evidence that structural deviations across NDPCs are systematically constrained by the brain’s intrinsic architecture. This shared organization provides a framework for reconciling individual variability with transdiagnostic similarities and motivates an integrative, systems-level understanding of mental health.

Background: Although cognitive behavioral therapy (CBT) is very effective in treating obsessive-compulsive disorder, not all patients benefit sufficiently from the planned duration of treatment. Objective: Does extending CBT beyond the regular treatment period lead to a significant reduction in the severity of obsessive-compulsive symptoms? Method: Analysis of evaluation data from routine treatment procedures that did not lead to remission after 40 sessions and were extended. Results: During the extension phase, symptom severity decreased with a large effect size and 43% of the patients achieved remission. Conclusion: Extension of CBT appears to be a sensible option in cases of insufficient therapeutic success in routine treatment.

Cognitive-behavioral therapy (CBT) is the first-line treatment for obsessive-compulsive disorder (OCD), yet a significant number of patients do not achieve remission or substantial symptom relief. This study aims to enhance the prediction of CBT outcomes in OCD by integrating demographic, clinical, and neuroimaging data using machine learning (ML) models. We conduct a comprehensive analysis on a well-characterized clinical sample, employing a rigorous validation scheme to avoid data leakage, and comparing multiple ML algorithms to minimize bias. Out of four different ML models trained on demographic and clinical data, structural MRI, and resting-state MRI functional connectivity data, no model was able to predict CBT success significantly above chance level in the present sample. Although clinical and demographic data enabled 64%-66% accuracy for predicting remission, this did not reach statistical significance after permutation testing. Pre-treatment symptom severity emerged numerically as the most promising predictor of remission, aligning with previous studies, but did not pass the significance threshold in the present study. Despite efforts to identify neuroimaging predictors, neither functional nor structural MRI features significantly contributed to the prediction models. These findings suggest that robust, individualized brain-based predictions for mental health outcomes remain challenging with the available data and sample size.

The ability to detect errors and adjust one's own behavior accordingly is fundamental to successful adaptations in dynamic environments. Errors elicit a negative frontocentral deflection in the event-related potential, i.e., error-related negativity (ERN). Moreover, they lead to an oscillatory burst in theta and delta ranges. Theta has been proposed to reflect a mechanism communicating the need for cognitive control across prefrontal brain regions. Error processing and theta and delta power have been repeatedly shown to be increased in patients with obsessive-compulsive disorder (OCD). In addition, OCD patients show reduced adjustments in error monitoring under speed and accuracy instructions. However, it remains unclear to what extent this finding is associated with altered theta and delta dynamics. Therefore, this study aims to analyze error-related oscillatory brain activity and behavioral adaptation in OCD patients under speed and accuracy instructions. We analyzed data from 25 healthy participants and 24 OCD patients applying a time-frequency analysis (complex Morlet wavelets) and a frequency-based coherence analysis to assess functional connectivity between medio-lateral prefrontal brain areas. Results indicate an overall increase in oscillatory activity due to the accuracy-focused instruction. However, no significant difference was found, i.e., no flexible adaptation between the speed and accuracy conditions for OCD patients as reflected in reduced variations of error-related delta activity. Furthermore, increased prefrontal connectivity explains exaggerated accuracy under speed focus as a result of overactive cognitive control in OCD patients. This supports the hypothesis that the flexibility of error monitoring is reduced, and cognitive control is increased in OCD patients.

In the context of personalized medicine, machine learning algorithms are growing in popularity. These algorithms require substantial information, which can be acquired effectively through the usage of previously gathered data. Open data and the utilization of synthetization techniques have been proposed to address this. In this paper, we propose and evaluate alternative approach that uses additional simulated data based on summary statistics published in the literature. The simulated data are used to pretrain random forests, which are afterwards fine-tuned on a real dataset. We compare the predictive performance of the new approach to random forests trained only on the real data. A Monte Carlo Cross Validation (MCCV) framework with 100 iterations was employed to investigate significance and stability of the results. Since a first study yielded inconclusive results, a second study with improved methodology (i.e., systematic information extraction and different prediction outcome) was conducted. In Study 1, some pretrained random forests descriptively outperformed the standard random forest. However, this improvement was not significant (t(99) = 0.89, p = 0.19). Contrary to expectations, in Study 2 the random forest trained only with the real data outperformed the pretrained random forests. We conclude with a discussion of challenges, such as the scarcity of informative publications, and recommendations for future research.

Obwohl Kognitive Verhaltenstherapie (KVT) bei Zwangsstörung sehr wirksam ist, profitieren nicht alle Behandelten in der geplanten Behandlungsdauer ausreichend. Führt die Verlängerung von KVT nach regulärer Behandlungsdauer zu einer bedeutsamen Reduktion der Schwere von Zwangssymptomen? Analyse der Evaluationsdaten von Routinebehandlungen, die nach 40 Sitzungen nicht zur Remission geführt haben und verlängert wurden. In der Verlängerungsphase verringerte sich die Symptomschwere mit großer Effektstärke. In der Verlängerung erreichten 43

Extinction of conditioned fear responses is assumed to relate to beneficial effects of exposure-based treatment, but few studies investigated the role of fear conditioning for exposure techniques in clinical settings. Specifically, no study addressed the relationship between psychophysiological indicators of extinction and outcome of exposure and response prevention (ERP) in adult obsessive-compulsive disorder (OCD). This study investigated whether skin conductance responses (SCRs) to threat and safety signals in a pre-treatment Pavlovian reversal learning experiment predicted the outcome of individual, manualized and ERP-based cognitive behavioral therapy (CBT) for 32 patients with OCD. In the first part of the experiment (acquisition stage) a picture of a face (CS+) was paired with an electrical stimulation (US) in one third of the trials while a second face (CS-) was never presented with the US. During a subsequent reversal stage, the formerly safe face was now coupled with the US (new CS+) and the former CS+ was no longer paired with the US (new CS-). Regression analyses showed that a larger SCR difference between threat and safety stimuli (CS + vs. CS-) during late reversal predicted both symptom reduction and remission at post-treatment, suggesting that adaptive learning skills may be important for successful ERP in OCD.

Cognitive behavioral therapy (CBT) is an effective treatment for obsessive-compulsive disorder (OCD). However, CBT does not lead to a satisfying symptom reduction in a considerable number of patients with OCD. The identification of variables that predict insufficient treatment response could improve efficient treatment selection and inform the development of specific augmentative treatments. In the current study, we tested whether prediction of treatment response can be improved by including neurobiological markers during working memory (WM) performance. Forty-four patients with a primary OCD diagnosis participated in an n-back WM task with varying WM load while functional Magnetic Resonance Imaging (fMRI) was performed. Subsequently, all patients received CBT in an outpatient clinic. WM load-dependent modulation of the blood-oxygen-level-dependent (BOLD) signal in a bilateral cluster in inferior/superior parietal lobule predicted CBT response over and above clinical and sociodemographic variables (p < 0.05). Higher modulation was associated with larger relative symptom reduction. The results of the current study indicate that the ability of the WM system to flexibly adapt to changing task demands might be a useful indicator of CBT response in OCD. Possibly, this mechanism facilitates relearning processes during exposure-based CBT. However, findings need to be replicated in larger samples.

Introduction Cognitive–behavioural therapy (CBT) works—but not equally well for all patients. Less than 50% of patients with internalising disorders achieve clinically meaningful improvement, with negative consequences for patients and healthcare systems. The research unit (RU) 5187 seeks to improve this situation by an in-depth investigation of the phenomenon of treatment non-response (TNR) to CBT. We aim to identify bio-behavioural signatures associated with TNR, develop predictive models applicable to individual patients and enhance the utility of predictive analytics by collecting a naturalistic cohort with high ecological validity for the outpatient sector. Methods and analysis The RU is composed of nine subprojects (SPs), spanning from clinical, machine learning and neuroimaging science and service projects to particular research questions on psychological, electrophysiological/autonomic, digital and neural signatures of TNR. The clinical study SP 1 comprises a four-centre, prospective-longitudinal observational trial where we recruit a cohort of 585 patients with a wide range of internalising disorders (specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalised anxiety disorder, obsessive–compulsive disorder, post-traumatic stress disorder, and unipolar depressive disorders) using minimal exclusion criteria. Our experimental focus lies on emotion (dys)-regulation as a putative key mechanism of CBT and TNR. We use state-of-the-art machine learning methods to achieve single-patient predictions, incorporating pretrained convolutional neural networks for high-dimensional neuroimaging data and multiple kernel learning to integrate information from various modalities. The RU aims to advance precision psychotherapy by identifying emotion regulation-based biobehavioural markers of TNR, setting up a multilevel assessment for optimal predictors and using an ecologically valid sample to apply findings in diverse clinical settings, thereby addressing the needs of vulnerable patients. Ethics and dissemination The study has received ethical approval from the Institutional Ethics Committee of the Department of Psychology at Humboldt-Universität zu Berlin (approval no. 2021-01) and the Ethics Committee of Charité-Universitätsmedizin Berlin (approval no. EA1/186/22). Results will be disseminated through peer-reviewed journals and presentations at national and international conferences. Deidentified data and analysis scripts will be made available to researchers within the RU via a secure server, in line with ethical guidelines and participant consent. In compliance with European and German data protection regulations, patient data will not be publicly available through open science frameworks but may be shared with external researchers on reasonable request and under appropriate data protection agreements. Trial registration number DRKS00030915.

Obsessive-compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D1 and D2 type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

Attentional biases to emotional information are assumed to play a crucial role in the onset and maintenance of depression. Moreover, recent studies show that biases may remain present in previously affected individuals during non-symptomatic stages even after acute depression has fully subsided. For example, in an investigation probing attentional disengagement from facial expressions of happiness, sadness, and disgust, never-depressed individuals showed speeded disengagement from disgusted expressions in comparison to happy faces, but this differential processing pattern was absent in currently euthymic individuals with a history of major depression. Building on these findings, the present follow-up study aimed to explore the predictive power of that previously described disengagement bias by assessing depressive symptoms in 63 initially euthymic individuals six months after they had participated in a gaze-contingent eye tracking task. Each participants’ mean difference in saccade latency to initiate eye movements away from facial expressions of happiness and disgust was assessed at baseline, and tested for associations with self-reported depressive symptom gains six months later. The individual’s difference between these two emotion conditions when performing attentional disengagement (ADΔhappiness-disgust) significantly predicted the occurrence of a reliable increase in depressive symptom severity at six months follow-up. This effect remained significant when controlling for baseline symptom severity and lifetime history of depression. Conversely, dimensional change in depressive symptom severity was not predicted by the ADΔhappiness-disgust score. We suggest that an individual difference score reflecting the ability to disengage attention from facial expressions of disgust versus happiness may be particularly useful in identifying individuals prone to experiencing reliable increases in depressive symptoms.