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Background Brain functional alterations during emotion processing in patients with major depressive disorder (MDD) compared with healthy control subjects (HCs) are frequently reported. However, evidence for functional correlates of emotion processing with regard to MDD trajectories is scarce. This study investigates the role of lifetime disease course for limbic brain activation during negative emotional face processing in patients with MDD. Methods In a large sample of patients with MDD (n = 333; 58.55% female) and HCs (n = 333; 60.06% female), brain activation was investigated during a negative emotional face-processing task within a cross-sectional design. Differences between HC and MDD groups were analyzed. Previous disease course, characterized by 2 components, namely hospitalization and duration of illness, was regressed on brain activation of the amygdala, (para-)hippocampus, and insula in patients with MDD. Results Patients with MDD showed increased activation in the amygdala, insula, and hippocampus compared with HCs (all p values corrected for familywise error [pFWE] .057). Conclusions This study investigated negative emotion processing in a large sample of patients with MDD and HCs. Our results confirm limbic hyperactivation in patients with MDD during negative emotion processing; however, this hyperactivation may resolve with a more severe lifetime disease course in the insula and (para-)hippocampus—brain regions involved in emotion processing and regulation. These findings need further replication in longitudinal studies.

BackgroundThe Covid-19 pandemic resulted in repeated, prolonged restrictions in daily life. Social distancing policies as well as health anxiety are thought to lead to mental health impairment. However, there is lack of longitudinal data identifying at-risk populations particularly vulnerable for elevated Covid-19-related distress.MethodsWe collected data of N=1268 participants (n=622 healthy controls (HC), and n=646 patients with major depression, bipolar disorder, schizophrenia or schizoaffective disorder) at baseline (2014-2018) and during the first lockdown in Germany (April-May 2020). We obtained information on Covid-19 restrictions (number and subjective impact of Covid-19 events), and Covid-19-related distress (i.e., subjective fear and isolation). Using multiple linear regression models including trait variables and individual Covid-19 impact, we sought to predict Covid-19-related distress.ResultsHC and patients reported similar numbers of Covid-19-related events, and similar subjective impact rating. They did not differ in Covid-19-related subjective fear. Patients reported significantly higher subjective isolation. 30.5% of patients reported worsened self-rated symptoms since the pandemic. Subjective fear in all participants was predicted by four variables: trait anxiety (STAI-T), conscientiousness (NEO-FFI), Covid-19 impact, and sex. Subjective isolation in HC was predicted by social support (FSozu), Covid-19 impact, age, and sex; in patients, it was predicted by social support and Covid-19 impact.ConclusionOur data shed light on differential effects of the pandemic in psychiatric patients and HC. They identify relevant, easy-to-obtain variables for risk profiles related to interindividual differences in Covid-19-related distress for direct translation into clinical practice.

Objectives Bipolar disorder (BD) has a highly heterogeneous clinical course that is characterized by relapses and increased health care utilization in a significant fraction of patients. A thorough understanding of factors influencing illness course is essential for predicting disorder severity and developing targeted therapies. Methods We performed polygenic score analyses in four cohorts (N = 954) to test whether the genetic risk for BD, schizophrenia, or major depression is associated with a severe course of BD. We analyzed BD patients with a minimum illness duration of five years. The severity of the disease course was assessed by using the number of hospitalizations in a mental health facility and a composite measure of longitudinal illness severity (OPCRIT item 90). Results Our analyses showed that higher polygenic scores for BD (β = 0.11, SE = 0.03, p = 1.17 × 10-3) and schizophrenia (β = 0.09, SE = 0.03, p = 4.24 × 10-3), but not for major depression, were associated with more hospitalizations. None of the investigated polygenic scores was associated with the composite measure of longitudinal illness severity (OPCRIT item 90). Limitations We could not account for non-genetic influences on disease course. Our clinical sample contained more severe cases. Conclusions This study demonstrates that the genetic risk burden for psychiatric illness is associated with increased health care utilization, a proxy for disease severity, in BD patients. The findings are in line with previous observations made for patients diagnosed with schizophrenia or major depression. Therefore, in the future psychiatric disorder polygenic scores might become helpful for stratifying patients with high risk of a chronic manifestation and predicting disease course.

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Background: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. Methods: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. Results: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18 < d < 0.22). More (hypo)manic episodes were associated with faster cortical thinning, primarily in the prefrontal cortex. Conclusions: In the hitherto largest longitudinal MRI study on BD, we did not detect accelerated cortical thinning but noted faster ventricular enlargements in BD. However, abnormal frontocortical thinning was observed in association with frequent manic episodes. Our study yields insights into disease progression in BD and highlights the importance of mania prevention in BD treatment.

Cognitive deficits are central attendant symptoms of major depressive disorder (MDD) with a crucial impact in patients' everyday life. Thus, it is of particular clinical importance to understand their pathophysiology. The aim of this study was to investigate a possible relationship between brain structure and cognitive performance in MDD patients in a well-characterized sample. N = 1007 participants (N

An interplay of early environmental and genetic risk factors with recent stressful life events (SLEs) in adulthood increases the risk for adverse mental health outcomes. The interaction of early risk and current SLEs on brain structure has hardly been investigated. Whole brain voxel-based morphometry analysis was performed in N = 786 (64.6% female, mean age = 33.39) healthy subjects to identify correlations of brain clusters with commonplace recent SLEs. Genetic and early environmental risk factors, operationalized as those for severe psychopathology (i.e., polygenic scores for neuroticism, childhood maltreatment, urban upbringing and paternal age) were assessed as modulators of the impact of SLEs on the brain. SLEs were negatively correlated with grey matter volume in the left medial orbitofrontal cortex (mOFC, FWE p = 0.003). This association was present for both, positive and negative, life events. Cognitive-emotional variables, i.e., neuroticism, perceived stress, trait anxiety, intelligence, and current depressive symptoms did not account for the SLE-mOFC association. Further, genetic and environmental risk factors were not correlated with grey matter volume in the left mOFC cluster and did not affect the association between SLEs and left mOFC grey matter volume. The orbitofrontal cortex has been implicated in stress-related psychopathology, particularly major depression in previous studies. We find that SLEs are associated with this area. Important early life risk factors do not interact with current SLEs on brain morphology in healthy subjects.

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1–3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.