Social anxiety disorder (SAD) is one of the most frequent anxiety disorders. The landmark meta-analysis of functional neuroimaging studies by Etkin and Wager (2007) revealed primarily the typical fear circuit as overactive in SAD. Since then, new methodological developments such as functional connectivity and more standardized structural analyses of grey and white matter have been developed. We provide a comprehensive update and a meta-analysis of neuroimaging studies in SAD since 2007 and present a new model of the neurobiology of SAD. We confirmed the hyperactivation of the fear circuit (amygdala, insula, anterior cingulate and prefrontal cortex) in SAD. In addition, task-related functional studies revealed hyperactivation of medial parietal and occipital regions (posterior cingulate, precuneus, cuneus) in SAD and a reduced connectivity between parietal and limbic and executive network regions. Based on the result of this meta-analysis and review, we present an updated model of SAD adopting a network-based perspective. The disconnection of the medial parietal hub in SAD extends current frameworks for future research in anxiety disorders.

BackgroundDepression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support.ObjectiveThe objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms.MethodsA total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery.ResultsA total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively.ConclusionsProxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital heal...

Background The death toll of COVID-19 topped 170,000 in Europe by the end of May 2020. COVID-19 has caused an immense psychological burden on the population, especially among doctors and nurses who are faced with high infection risks and increased workload. Objective The aim of this study was to compare the mental health of medical professionals with nonmedical professionals in different European countries during the COVID-19 pandemic. We hypothesized that medical professionals, particularly those exposed to COVID-19 at work, would have higher levels of depression, anxiety, and stress. We also aimed to determine their main stressors and most frequently used coping strategies during the crisis. Methods A cross-sectional online survey was conducted during peak COVID-19 months in 8 European countries. The questionnaire included demographic data and inquired whether the participants were exposed to COVID-19 at work or not. Mental health was assessed via the Depression Anxiety Stress Scales32 (23.53)–21 (DASS-21). A 12-item checklist on preferred coping strategies and another 23-item questionnaire on major stressors were completed by medical professionals. Results The sample (N=609) consisted of 189 doctors, 165 nurses, and 255 nonmedical professionals. Participants from France and the United Kingdom reported experiencing severe/extremely severe depression, anxiety, and stress more often compared to those from the other countries. Nonmedical professionals had significantly higher scores for depression and anxiety. Among medical professionals, no significant link was reported between direct contact with patients with COVID-19 at work and anxiety, depression, or stress. “Uncertainty about when the epidemic will be under control” caused the most amount of stress for health care professionals while “taking protective measures” was the most frequently used coping strategy among all participants. Conclusions COVID-19 poses a major challenge to the mental health of working professionals as a considerable proportion of our participants showed high values for depression, anxiety, and stress. Even though medical professionals exhibited less mental stress than nonmedical professionals, sufficient help should be offered to all occupational groups with an emphasis on effective coping strategies.

Social anxiety disorder (SAD) is the second leading anxiety disorder. On the functional neurobiological level, specific brain regions involved in the processing of anxiety-laden stimuli and in emotion regulation have been shown to be hyperactive and hyper-responsive in SAD such as amygdala, insula and orbito- and prefrontal cortex. On the level of brain structure, prior studies on anatomical differences in SAD resulted in mixed and partially contradictory findings. Based on previous functional and anatomical models of SAD, this study examined cortical thickness in structural magnetic resonance imaging data of 46 patients with SAD without comorbidities (except for depressed episode in one patient) compared with 46 matched healthy controls in a region of interest-analysis and in whole-brain. In a theory-driven ROI-analysis, cortical thickness was increased in SAD in left insula, right anterior cingulate and right temporal pole. Furthermore, the whole-brain analysis revealed increased thickness in right dorsolateral prefrontal and right parietal cortex. This study detected no regions of decreased cortical thickness or brain volume in SAD. From the perspective of brain networks, these findings are in line with prior functional differences in salience networks and frontoparietal networks associated with executive-controlling and attentional functions.

BackgroundDizziness is frequently encountered in medical practice, often takes a chronic course and can impair the health related quality of life (HRQoL). However results on the extent of this impairment of HRQoL are mixed. Furthermore, the relationship between dizziness and the HRQoL is only partially understood. The role of clinical symptoms of dizziness and psychosocial factors such as emotional distress on this relationship is for the most part unknown.MethodsThe cross-sectional study evaluated the HRQoL in 203 patients suffering from dizziness, using the Medical Outcomes Studies 36-Item Short-Form Health-Survey (SF-36). The results were correlated with the severity of dizziness, using the Dizziness Handicap-Inventory (DHI), with emotional distress, using the Hospital Anxiety and Depression-Scale (HADS) and with further clinical symptoms and psychosocial parameters. In a multivariate hierarchical regression analysis associated variables which explain significant variance of the mental and physical HRQoL (MCS-36, PCS-36) were identified.ResultsPatients suffering from dizziness showed a markedly reduced mental and physical HRQoL. Higher DHI and HADS scores were correlated with lower MCS-36 and PCS-36 scores. Taken together DHI and vertigo characteristics of dizziness explained 38% of the variance of PCS-36. Overall explained variance of PCS-36 was 45%. HADS and living with a significant other explained 66% of the variance of MCS-36 (overall variance explained: 69%).ConclusionBoth the physical and mental HRQoL are significantly impaired in patients with dizziness. While the impairment in PCS-36 can be explained by clinical symptoms of the dizziness, MCS-36 impairment is largely associated with psychosocial factors. To improve the patient’s overall well-being significantly and permanently doctors have to keep in mind both, the clinical symptoms and the psychosocial factors. Therefore, in addition to the physical examination doctors should integrate a basic psychological examination into the daily routine with dizziness patients.