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Background: Childhood maltreatment (CM) represents a potent risk factor for major depressive disorder (MDD), including poorer treatment response. Altered resting-state connectivity in the fronto-limbic system has been reported in maltreated individuals. However, previous results in smaller samples differ largely regarding localization and direction of effects., Methods: We included healthy and depressed samples [ n = 624 participants with MDD; n = 701 healthy control (HC) participants] that underwent resting-state functional MRI measurements and provided retrospective self-reports of maltreatment using the Childhood Trauma Questionnaire. A-priori defined regions of interest [ROI; amygdala, hippocampus, anterior cingulate cortex (ACC)] were used to calculate seed-to-voxel connectivities., Results: No significant associations between maltreatment and resting-state connectivity of any ROI were found across MDD and HC participants and no interaction effect with diagnosis became significant. Investigating MDD patients only yielded maltreatment-associated increased connectivity between the amygdala and dorsolateral frontal areas [ p FDR < 0.001; η 2 partial = 0.050; 95%-CI (0.023-0.085)]. This effect was robust across various sensitivity analyses and was associated with concurrent and previous symptom severity. Particularly strong amygdala-frontal associations with maltreatment were observed in acutely depressed individuals [ n = 264; p FDR < 0.001; η 2 partial = 0.091; 95%-CI (0.038-0.166)). Weaker evidence - not surviving correction for multiple ROI analyses - was found for altered supracallosal ACC connectivity in HC individuals associated with maltreatment., Conclusions: The majority of previous resting-state connectivity correlates of CM could not be replicated in this large-scale study. The strongest evidence was found for clinically relevant maltreatment associations with altered adult amygdala-dorsolateral frontal connectivity in depression. Future studies should explore the relevance of this pathway for a maltreated subgroup of MDD patients.

Repeated hospitalizations are a characteristic of severe disease courses in patients with affective disorders (PAD). To elucidate how a hospitalization during a nine-year follow-up in PAD affects brain structure, a longitudinal case-control study (mean [SD] follow-up period 8.98 [2.20] years) was conducted using structural neuroimaging. We investigated PAD (N = 38) and healthy controls (N = 37) at two sites (University of Münster, Germany, Trinity College Dublin, Ireland). PAD were divided into two groups based on the experience of in-patient psychiatric treatment during follow-up. Since the Dublin-patients were outpatients at baseline, the re-hospitalization analysis was limited to the Münster site (N = 52). Voxel-based morphometry was employed to examine hippocampus, insula, dorsolateral prefrontal cortex and whole-brain gray matter in two models: (1) group (patients/controls)×time (baseline/follow-up) interaction; (2) group (hospitalized patients/not-hospitalized patients/controls)×time interaction. Patients lost significantly more whole-brain gray matter volume of superior temporal gyrus and temporal pole compared to HC (p FWE  = 0.008). Patients hospitalized during follow-up lost significantly more insular volume than healthy controls (p FWE  = 0.025) and more volume in their hippocampus compared to not-hospitalized patients (p FWE  = 0.023), while patients without re-hospitalization did not differ from controls. These effects of hospitalization remained stable in a smaller sample excluding patients with bipolar disorder. PAD show gray matter volume decline in temporo-limbic regions over nine years. A hospitalization during follow-up comes with intensified gray matter volume decline in the insula and hippocampus. Since hospitalizations are a correlate of severity, this finding corroborates and extends the hypothesis that a severe course of disease has detrimental long-term effects on temporo-limbic brain structure in PAD., (© 2023. The Author(s).)

Objective: A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia., Method: Here, we examine this issue through two approaches: (a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N ∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA-Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior., Results: We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15-0.97; r range: 0.21-0.94) reported in the literature. Two common multi-item instruments, the Columbia Suicide Severity Rating Scale and the Beck Scale for Suicidal Ideation were highly correlated with each other (r = 0.83). Sensitivity analyses identified sources of heterogeneity such as the time frame of the instrument and whether it relies on self-report or a clinical interview. Finally, construct-specific analyses suggest that suicide ideation items from common psychiatric questionnaires are most concordant with the suicide ideation construct of multi-item instruments., Conclusions: Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behavior but share a modest core factor with single suicidal ideation items. Retrospective, multisite collaborations including distinct instruments should be feasible provided they harmonize across instruments or focus on specific constructs of suicidality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: There is a lack of knowledge regarding the relationship between dimensional psychopathological syndromes and neurocognitive functions, particularly across the major psychiatric disorders (i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), and Schizophrenia (SZ))., Method: SANS, SAPS, HAMA, HAM-D, and YMRS were assessed in 1064 patients meeting DSM-IV-TR criteria for MDD, BD, SZ or schizoaffective disorder (SZA). In addition, a comprehensive neuropsychological test battery was administered. Psychopathological syndromes derived from factor analysis and present state of illness were used to explore psychopathology-cognition relationships. Correlational analyses were corrected for age, sex, verbal IQ, years of education, and DSM-IV-TR diagnosis. Age of onset and total duration of hospitalizations as proxies for illness severity were tested as moderators on the cognition - psychopathology relationship., Results: The negative syndrome, positive formal thought disorder as well as the paranoid-hallucinatory syndrome exhibited associations with neuro-cognition in an illness state-dependent manner, while the psychopathological factors depression and increased appetite only showed weak associations. Illness severity showed moderating effects on the neurocognitive-psychopathology relationship only for the negative syndrome and positive formal thought disorder., Limitations: No healthy control subjects were entered into the analyses because of lack of variance in psychopathological symptoms, which prevents from drawing conclusions regarding the relative level of potential cognitive impairments., Conclusions: This study suggests the relationship of neuro-cognition and psychopathology to be highly state of illness-dependent across affective and psychotic disorders. Results hint at the moderating effects of illness severity on psychopathological factors that might be more treatment resistant., Competing Interests: Conflict of interest Tilo Kircher received unrestricted educational grants from Servier, Janssen, Recordati, Aristo, Otsuka, neuraxpharm. All other authors declare no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Epidemiological studies have shown that gestational age and birth weight are linked to cognitive performance in adults. On a neurobiological level, this effect is hypothesized to be related to cortical gyrification, which is determined primarily during fetal development. The relationships between gestational age, gyrification and specific cognitive abilities in adults are still poorly understood. In 542 healthy participants, gyrification indices were calculated from structural magnetic resonance imaging T1 data at 3 T using CAT12. After applying a battery of neuropsychological tests, neuropsychological factors were extracted with a factor analysis. We conducted regressions to test associations between gyrification and gestational age as well as birth weight. Moderation analyses explored the relationships between gestational age, gyrification and neuropsychological factors. Gestational age is significantly positively associated with cortical folding in the left supramarginal, bilaterally in the superior frontal and the lingual cortex. We extracted two neuropsychological factors that describe language abilities and working memory/attention. The association between gyrification in the left superior frontal gyrus and working memory/attention was moderated by gestational age. Further, the association between gyrification in the left supramarginal cortex and both, working memory/attention as well as language, were moderated by gestational age. Gyrification is associated with gestational age and related to specific neuropsychological outcomes in healthy adulthood. Implications from these findings for the cortical neurodevelopment of cognitive domains and mental health are discussed., (© 2022. The Author(s).)

Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health., (© 2023. The Author(s).)

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, p FWE  = 0.037; right: d = -0.104, p FWE  = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, p FWE  = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, p FWE  < 0.001; right: d = -0.158, p FWE  < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, p FWE  = 0.006; right: d = 0.099, p FWE  = 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., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact., Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations., Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI., Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Background: Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks., Methods: Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength., Results: All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course., Conclusions: Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.

Expression of polysialic acid (polySia) in the adult brain is enriched in areas of continuous neurogenesis and plasticity such as the hippocampus. Genome-wide association studies identified variants of glycosylation enzyme-encoding genes, required for the generation of polySia, to be associated with the development of schizophrenia and bipolar disorder. Here, we report that serum levels of polySia are increased in patients with schizophrenia spectrum disorder compared to patients with major depressive disorders or demographically matched healthy controls. Furthermore, elevated polySia serum levels are associated with structural hippocampal gray matter decline in schizophrenia spectrum and bipolar disorder. In patients with schizophrenia spectrum disorder, polySia serum levels correlate with the number, duration of disease-related hospitalizations, early retirement and medical leave as estimators of detrimental long-term disease trajectories. Our data show that polySia serum levels are linked to structural hippocampal brain changes in schizophrenia spectrum and bipolar disorders, and suggest a contribution of polySia to the pathophysiology of these diseases., (© 2023. The Author(s).)

Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

Background: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects., Methods: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices., Results: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis., Conclusions: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Retrospective self-reports of childhood maltreatment (CM) are widely used. However, their validity has been questioned due to potential depressive bias. Yet, investigations of this matter are sparse. Thus, we investigated to what extent retrospective maltreatment reports vary in relation to longitudinal changes in depressive symptomatology. Two-year temporal stability of maltreatment reports was assessed via the Childhood Trauma Questionnaire (CTQ). Diagnosis of major depressive disorder (MDD) and depressive symptoms were assessed using the Structured Clinical Interview for DSM-IV and the Beck Depression Inventory (BDI). We included a total of n = 419 healthy controls (HC), n = 347 MDD patients, and a subsample with an initial depressive episode between both assessments ( n = 27), from two independent cohorts (Marburg-Münster-affective-disorders-cohort-study and Münster-Neuroimaging-cohort). Analysis plan and hypotheses were preregistered prior to data analysis. Dimensional CTQ scores were highly stable in HC and MDD across both cohorts (ICC = .956; 95% CI [.949, .963] and ICC = .950; 95% CI [.933, .963]) and temporal stability did not differ between groups. Stability was lower for cutoff-based binary CTQ scores ( K = .551; 95% CI [.479, .622] and K = .507; 95% CI [.371, .640]). Baseline dimensional CTQ scores were associated with concurrent and future BDI scores. However, longitudinal changes in BDI scores predicted variability in dimensional CTQ scores only to a small extent across cohorts ( b = 0.101, p = .009, R ² = .021 and b = 0.292, p = .320), with the effect being driven by emotional maltreatment subscales. Findings suggest that the CTQ provides temporally stable self-reports of CM in healthy and depressed populations and is only marginally biased by depressive symptomatology. A dimensional rather than binary conceptualization of maltreatment is advised for improving psychometric quality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Genetic variations affecting dopaminergic neuromodulation such as the DRD2/ANKK1 and the COMT Val158Met polymorphisms contribute to goal-directed behavior that requires a balance between stabilization and updating of current states and behaviors. Dopamine is also thought to be relevant for encoding of surprise signals to sensory input and adaptive learning. A link between goal-directed behavior and learning from surprise is therefore plausible. In the present fMRI study, we investigated whether DRD2 and COMT polymorphisms are related to behavioral responses and neural signals in the caudate nucleus and dlPFC during updating or stabilizing internal models of predictable digit sequences. To-be-detected switches between sequences and to-be-ignored digit omissions within a sequence varied by information-theoretic quantities of surprise and entropy. We found that A1 noncarriers and Val-carriers showed a lower response threshold along with increased caudate and dlPFC activation to surprising switches compared with A1-carriers and Met-homozygotes, whose dlPFC activity increased with decreasing switch surprise. In contrast, there were overall smaller differences in behavioral and neural modulation by drift surprise. Our results suggest that the impact of dopamine-relevant polymorphisms in the flexibility-stability trade-off may result in part from the role of dopamine in encoding the weight afforded to events requiring updating or stabilization., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Personality traits influence risk for suicidal behavior. We examined phenotype- and genotype-level associations between the Big Five personality traits and suicidal ideation and attempt in major depressive, bipolar and schizoaffective disorder, and schizophrenia patients (N = 3012) using fixed- and random-effects inverse variance-weighted meta-analyses. Suicidal ideations were more likely to be reported by patients with higher neuroticism and lower extraversion phenotypic scores, but showed no significant association with polygenic load for these personality traits. Our findings provide new insights into the association between personality and suicidal behavior across mental illnesses and suggest that the genetic component of personality traits is unlikely to have strong causal effects on suicidal behavior., (© 2022. The Author(s).)

Identifying brain alterations associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and improving early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth. We performed analyses in three separate stages, in samples ranging from most to least homogeneous in terms of suicide assessment instrument and mental disorder. First, in a sample of 577 young people with mood disorders, in which STBs were assessed with the Columbia Suicide Severity Rating Scale (C-SSRS). Second, in a sample of young people with mood disorders, in which STB were assessed using different instruments, MRI metrics were compared among healthy controls without STBs (HC; N = 519), clinical controls with a mood disorder but without STBs (CC; N = 246) and young people with current suicidal ideation (N = 223). In separate analyses, MRI metrics were compared among HCs (N = 253), CCs (N = 217), and suicide attempters (N = 64). Third, in a larger transdiagnostic sample with various assessment instruments (HC = 606; CC = 419; Ideation = 289; HC = 253; CC = 432; Attempt=91). In the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and a history of actual suicide attempts (N = 163) than those without a lifetime suicide attempt (N = 323; FDR-p = 0.035, Cohen's d = 0.34). No associations with suicidal ideation were found. When examining more heterogeneous samples, we did not observe significant associations. Lower frontal pole surface area may represent a vulnerability for a (non-interrupted and non-aborted) suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk., (© 2022. Crown.)

Bipolar disorder (BD) is a chronic mood disorder characterized by manic and depressive episodes. Dysregulation of neuroplasticity and calcium homeostasis are frequently observed in BD patients, but the underlying molecular mechanisms are largely unknown. Here, we show that miR-499-5p regulates dendritogenesis and cognitive function by downregulating the BD risk gene CACNB2. miR-499-5p expression is increased in peripheral blood of BD patients, as well as in the hippocampus of rats which underwent juvenile social isolation. In rat hippocampal neurons, miR-499-5p impairs dendritogenesis and reduces surface expression and activity of the L-type calcium channel Cav1.2. We further identified CACNB2, which encodes a regulatory β-subunit of Cav1.2, as a direct functional target of miR-499-5p in neurons. miR-499-5p overexpression in the hippocampus in vivo induces short-term memory impairments selectively in rats haploinsufficient for the Cav1.2 pore forming subunit Cacna1c. In humans, miR-499-5p expression is negatively associated with gray matter volumes of the left superior temporal gyrus, a region implicated in auditory and emotional processing. We propose that stress-induced miR-499-5p overexpression contributes to dendritic impairments, deregulated calcium homeostasis, and neurocognitive dysfunction in BD., (© 2022 The Authors. Published under the terms of the CC BY 4.0 license.)

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD, schizophrenia, and schizoaffective disorder) overlap in symptomatology, risk factors, genetics, and other biological measures. Based on previous findings, it remains unclear what transdiagnostic regional gray matter volume (GMV) alterations exist across these disorders, and with which factors they are associated. GMV (3-T magnetic resonance imaging) was compared between healthy controls (HC; n = 110), DSM-IV-TR diagnosed MDD (n = 110), BD (n = 110), and SSD patients (n = 110), matched for age and sex. We applied a conjunction analysis to identify shared GMV alterations across the disorders. To identify potential origins of identified GMV clusters, we associated them with early and current risk and protective factors, psychopathology, and neuropsychology, applying multiple regression models. Common to all diagnoses (vs. HC), we identified GMV reductions in the left hippocampus. This cluster was associated with the neuropsychology factor working memory/executive functioning, stressful life events, and with global assessment of functioning. Differential effects between groups were present in the left and right frontal operculae and left insula, with volume variances across groups highly overlapping. Our study is the first with a large, matched, transdiagnostic sample to yield shared GMV alterations in the left hippocampus across major mental disorders. The hippocampus is a major network hub, orchestrating a range of mental functions. Our findings underscore the need for a novel stratification of mental disorders, other than categorical diagnoses., (© 2022. The Author(s).)

Importance: Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping., Objective: To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC)., Design, Setting, and Participants: In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022., Main Outcomes and Measures: Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation., Results: Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control)., Conclusions and Relevance: In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.

Background: Among mental disorders, major depressive disorder (MDD) is highly prevalent and associated with emotional dysfunctions linked to activity alterations in the brain, mainly in prefrontal regions, the insula, the anterior cingulate cortex and the amygdala. However, this evidence is heterogeneous, perhaps because magnetic resonance imaging (MRI) studies on MDD tend to neglect comorbid anxiety (COM-A)., Methods: To address this, here a sample of age- and sex-matched patients, n MDD  = 90 and n COM-A  = 85, underwent functional MRI to assess neurofunctional group differences during a negative emotional face-matching task using a hypothesis-driven region of interest approach (dorsolateral prefrontal cortex, insula, anterior cingulate cortex, amygdala) and an explorative whole-brain approach. We also assessed these relationships with state-trait anxiety measures, a state depression measure, general functioning and medication load., Results: During face processing, COM-A (compared to MDD) had significantly increased bilateral insula activity. No activity differences were found in the anterior cingulate cortex or the amygdala. Whole-brain analyses revealed increased inferior temporal activation and frontal activation (comprising the inferior and middle frontal gyrus) in COM-A that was positively linked to state anxiety as well as general functioning across groups., Limitations: Still, the lack of a healthy control and small effects mean this study should be replicated to further interpret the results., Conclusions: The findings highlight a discriminative activation pattern between MDD and COM-A regarding emotion processing and may present a correlate of potentially anxiety-related psychopathology. In future, further investigations in potential discriminative activity patterns could help to elucidate the origin, development and treatment of depression., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: The diathesis-stress model of major depressive disorder (MDD) predicts interactions of recent stressful life events (SLEs) in adulthood and early developmental risk factors. We tested, for the first time, the diathesis stress model on brain structure in a large group of MDD patients., Methods: Structural magnetic resonance imaging data of 1465 participants (656 with lifetime diagnosis MDD; 809 healthy controls) were analyzed using voxel-based morphometry to identify clusters associated with recent SLEs (Life Events Questionnaire). Those clusters were then examined for group (healthy/MDD) × early developmental risk (operationalized as childhood abuse [Childhood Trauma Questionnaire] and a major psychiatric disorder [i.e., MDD, bipolar disorder, schizophrenia, and schizoaffective disorder] in a first-degree relative) × recent SLEs three-way interactions on grey matter volume., Results: There was a group × childhood abuse × recent SLEs interaction on left medial orbitofrontal cortex grey matter volume. This three-way interaction arose because childhood abuse and recent SLEs interacted in MDD subjects but not in healthy subjects., Limitations: We are not able to draw conclusions about the cause and effect relationship due to our cross-sectional study design., Conclusions: Our data provides evidence for an interplay between orbitofrontal cortex structure, childhood abuse and recent SLEs. These factors have previously been linked to MDD and their complex interaction contributes to the pathogenesis of MDD., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Importance: Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression., Objective: To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables., Design, Setting, and Participants: This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022., Main Outcomes and Measures: Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status., Results: A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables., Conclusions and Relevance: Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

Former prospective studies showed that the occurrence of relapse in Major Depressive Disorder (MDD) is associated with volume loss in the insula, hippocampus and dorsolateral prefrontal cortex (DLPFC). However, these studies were confounded by the patient's lifetime disease history, as the number of previous episodes predict future recurrence. In order to analyze neural correlates of recurrence irrespective of prior disease course, this study prospectively examined changes in brain structure in patients with first-episode depression (FED) over 2 years. N = 63 FED patients and n = 63 healthy controls (HC) underwent structural magnetic resonance imaging at baseline and after 2 years. According to their disease course during the follow-up interval, patients were grouped into n = 21 FED patients with recurrence (FEDrec) during follow-up and n = 42 FED patients with stable remission (FEDrem). Gray matter volume changes were analysed using group by time interaction analyses of covariance for the DLPFC, hippocampus and insula. Significant group by time interactions in the DLPFC and insula emerged. Pairwise comparisons showed that FEDrec had greater volume decline in the DLPFC and insula from baseline to follow-up compared with FEDrem and HC. No group by time interactions in the hippocampus were found. Cross-sectional analyses at baseline and follow-up revealed no differences between groups. This longitudinal study provides evidence for neural alterations in the DLPFC and insula related to a detrimental course in MDD. These effects of recurrence are already detectable at initial stages of MDD and seem to occur without any prior disease history, emphasizing the importance of early interventions preventing depressive recurrence., (© 2022. The Author(s).)

Background: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., Methods: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., Results: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., Conclusions: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry., Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles., Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex., Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Relapses in major depression are frequent and are associated with a high burden of disease. Although short-term studies suggest a normalisation of depression-associated brain functional alterations directly after treatment, long-term investigations are sparse., Aims: To examine brain function during negative emotion processing in association with course of illness over a 2-year span., Method: In this prospective case-control study, 72 in-patients with current depression and 42 healthy controls were investigated during a negative emotional face processing paradigm, at baseline and after 2 years. According to their course of illness during the study interval, patients were divided into subgroups ( n = 25 no-relapse, n = 47 relapse). The differential changes in brain activity were investigated by a group × time analysis of covariance for the amygdala, hippocampus, insula and at whole-brain level., Results: A significant relapse × time interaction emerged within the amygdala ( P TFCE-FWE = 0.011), insula ( P TFCE-FWE = 0.001) and at the whole-brain level mainly in the temporal and prefrontal cortex ( P TFCE-FWE = 0.027), resulting from activity increases within the no-relapse group, whereas in the relapse group, activity decreased during the study interval. At baseline, the no-relapse group showed amygdala, hippocampus and insula hypoactivity compared with healthy controls and the relapse group., Conclusions: This study reveals course of illness-associated activity changes in emotion processing areas. Patients in full remission show a normalisation of their baseline hypo-responsiveness to the activation level of healthy controls after 2 years. Brain function during emotion processing could further serve as a potential predictive marker for future relapse.

Stressful life events (SLEs) in adulthood are a risk factor for various disorders such as depression, cancer or infections. Part of this risk is mediated through pathways altering brain physiology and structure. There is a lack of longitudinal studies examining associations between SLEs and brain structural changes. High-resolution structural magnetic resonance imaging data of 212 healthy subjects were acquired at baseline and after 2 years. Voxel-based morphometry was used to identify associations between SLEs using the Life Events Questionnaire and grey matter volume (GMV) changes during the 2-year period in an ROI approach. Furthermore, we assessed adverse childhood experiences as a possible moderator of SLEs-GMV change associations. SLEs were negatively associated with GMV changes in the left medial prefrontal cortex. This association was stronger when subjects had experienced adverse childhood experiences. The medial prefrontal cortex has previously been associated with stress-related disorders. The present findings represent a potential neural basis of the diathesis-stress model of various disorders., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Factorial dimensions and neurobiological underpinnings of formal thought disorders (FTD) have been extensively investigated in schizophrenia spectrum disorders (SSD). However, FTD are also highly prevalent in other disorders. Still, there is a lack of knowledge about transdiagnostic, structural brain correlates of FTD. In N = 1071 patients suffering from DSM-IV major depressive disorder, bipolar disorder, or SSD, we calculated a psychopathological factor model of FTD based on the SAPS and SANS scales. We tested the association of FTD dimensions with 3 T MRI measured gray matter volume (GMV) and white matter fractional anisotropy (FA) using regression and interaction models in SPM12. We performed post hoc confirmatory analyses in diagnostically equally distributed, age- and sex-matched sub-samples to test whether results were driven by diagnostic categories. Cross-validation (explorative and confirmatory) factor analyses revealed three psychopathological FTD factors: disorganization, emptiness, and incoherence. Disorganization was negatively correlated with a GMV cluster comprising parts of the middle occipital and angular gyri and positively with FA in the right posterior cingulum bundle and inferior longitudinal fascicle. Emptiness was negatively associated with left hippocampus and thalamus GMV. Incoherence was negatively associated with FA in bilateral anterior thalamic radiation, and positively with the hippocampal part of the right cingulum bundle. None of the gray or white matter associations interacted with diagnosis. Our results provide a refined mapping of cross-disorder FTD phenotype dimensions. For the first time, we demonstrated that their neuroanatomical signatures are associated with language-related gray and white matter structures independent of diagnosis., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: The investigation of disease course-associated brain structural alterations in Major Depressive Disorder (MDD) have resulted in heterogeneous findings, possibly due to low reliability of single clinical variables used for defining disease course. The present study employed a principal component analysis (PCA) on multiple clinical variables to investigate effects of cumulative lifetime illness burden on brain structure in a large and heterogeneous sample of MDD patients., Methods: Gray matter volumes (GMV) was estimated in n = 681 MDD patients (mean age: 35.87 years; SD = 12.89; 66.6% female) using voxel-based-morphometry. Five clinical variables were included in a PCA to obtain components reflecting disease course to associate resulting components with GMVs., Results: The PCA yielded two main components: Hospitalization reflected by patients' frequency and duration of inpatient treatment and Duration of Illness reflected by the frequency and duration of depressive episodes. Hospitalization revealed negative associations with bilateral dorsolateral prefrontal cortex (DLPFC) and left insula volumes. Duration of Illness showed significant negative associations with left hippocampus and right DLPFC volumes. Results in the DLPFC and hippocampus remained significant after additional control for depressive symptom severity, psychopharmacotherapy, psychiatric comorbidities, and remission status., Conclusion: This study shows that a more severe and chronic lifetime disease course in MDD is associated with reduced volume in brain regions relevant for executive and cognitive functions and emotion regulation in a large sample of patients representing the broad heterogeneity of MDD disease course. These findings were only partly influenced by other clinical characteristics (e.g., remission status, psychopharmacological treatment)., (© 2022 The Authors. Depression and Anxiety published by Wiley Periodicals LLC.)

Background: Eighty percent of all patients suffering from major depressive disorder (MDD) relapse at least once in their lifetime. Thus, understanding the neurobiological underpinnings of the course of MDD is of utmost importance. A detrimental course of illness in MDD was most consistently associated with superior longitudinal fasciculus (SLF) fiber integrity. As similar associations were, however, found between SLF fiber integrity and acute symptomatology, this study attempts to disentangle associations attributed to current depression from long-term course of illness., Methods: A total of 531 patients suffering from acute ( N = 250) or remitted ( N = 281) MDD from the FOR2107-cohort were analyzed in this cross-sectional study using tract-based spatial statistics for diffusion tensor imaging. First, the effects of disease state (acute v . remitted), current symptom severity (BDI-score) and course of illness (number of hospitalizations) on fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity were analyzed separately. Second, disease state and BDI-scores were analyzed in conjunction with the number of hospitalizations to disentangle their effects., Results: Disease state ( p FWE < 0.042) and number of hospitalizations ( p FWE < 0.032) were associated with decreased FA and increased MD and RD in the bilateral SLF. A trend was found for the BDI-score ( p FWE > 0.067). When analyzed simultaneously only the effect of course of illness remained significant ( p FWE < 0.040) mapping to the right SLF., Conclusions: Decreased FA and increased MD and RD values in the SLF are associated with more hospitalizations when controlling for current psychopathology. SLF fiber integrity could reflect cumulative illness burden at a neurobiological level and should be targeted in future longitudinal analyses.

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Schizotypy is a putative risk phenotype for psychosis liability, but the overlap of its genetic architecture with schizophrenia is poorly understood., Methods: We tested the hypothesis that dimensions of schizotypy (assessed with the SPQ-B) are associated with a polygenic risk score (PRS) for schizophrenia in a sample of 623 psychiatrically healthy, non-clinical subjects from the FOR2107 multi-centre study and a second sample of 1133 blood donors., Results: We did not find correlations of schizophrenia PRS with either overall SPQ or specific dimension scores, nor with adjusted schizotypy scores derived from the SPQ (addressing inter-scale variance). Also, PRS for affective disorders (bipolar disorder and major depression) were not significantly associated with schizotypy., Conclusions: This important negative finding demonstrates that despite the hypothesised continuum of schizotypy and schizophrenia, schizotypy might share less genetic risk with schizophrenia than previously assumed (and possibly less compared to psychotic-like experiences).

Background: Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if they do, how these effects could contribute to the disease course of MDD., Methods: This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic patients with MDD and 539 healthy control subjects., Results: Polygenic risk score for T2D across MDD patients and healthy control subjects was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this fractional anisotropy variation may mediate the association between polygenic risk score and cognitive performance., Conclusions: Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in patients with MDD and healthy control subjects, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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 [p FWE ] < .045). The hospitalization component showed negative associations with brain activation in the bilateral insula (right: p FWE  = .026, left: p FWE  = .019) and (para-)hippocampus (right: p FWE  = .038, left: p FWE  = .031). No significant association was found for the duration of illness component (all p FWE > .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., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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 MDD  = 482, healthy controls (HC): N HC  = 525) were selected from the FOR2107 cohort for this diffusion-tensor imaging study employing tract-based spatial statistics. We conducted a principal component analysis (PCA) to reduce neuropsychological test results, and to discover underlying factors of cognitive performance in MDD patients. We tested the association between fractional anisotropy (FA) and diagnosis (MDD vs. HC) and cognitive performance factors. The PCA yielded a single general cognitive performance factor that differed significantly between MDD patients and HC (P < 0.001). We found a significant main effect of the general cognitive performance factor in FA (P tfce-FWE  = 0.002) in a large bilateral cluster consisting of widespread frontotemporal-association fibers. In MDD patients this effect was independent of medication intake, the presence of comorbid diagnoses, the number of previous hospitalizations, and depressive symptomatology. This study provides robust evidence that white matter disturbances and cognitive performance seem to be associated. This association was independent of diagnosis, though MDD patients show more pronounced deficits and lower FA values in the global white matter fiber structure. This suggests a more general, rather than the depression-specific neurological basis for cognitive deficits., (© 2021. The Author(s).)

Major Depressive Disorder (MDD) often is associated with significant cognitive dysfunction. We conducted a meta-analysis of genome-wide interaction of MDD and cognitive function using data from four large European cohorts in a total of 3510 MDD cases and 6057 controls. In addition, we conducted analyses using polygenic risk scores (PRS) based on data from the Psychiatric Genomics Consortium (PGC) on the traits of MDD, Bipolar disorder (BD), Schizophrenia (SCZ), and mood instability (MIN). Functional exploration contained gene expression analyses and Ingenuity Pathway Analysis (IPA ® ). We identified a set of significantly interacting single nucleotide polymorphisms (SNPs) between MDD and the genome-wide association study (GWAS) of cognitive domains of executive function, processing speed, and global cognition. Several of these SNPs are located in genes expressed in brain, with important roles such as neuronal development (REST), oligodendrocyte maturation (TNFRSF21), and myelination (ARFGEF1). IPA ® identified a set of core genes from our dataset that mapped to a wide range of canonical pathways and biological functions (MPO, FOXO1, PDE3A, TSLP, NLRP9, ADAMTS5, ROBO1, REST). Furthermore, IPA ® identified upstream regulator molecules and causal networks impacting on the expression of dataset genes, providing a genetic basis for further clinical exploration (vitamin D receptor, beta-estradiol, tadalafil). PRS of MIN and meta-PRS of MDD, MIN and SCZ were significantly associated with all cognitive domains. Our results suggest several genes involved in physiological processes for the development and maintenance of cognition in MDD, as well as potential novel therapeutic agents that could be explored in patients with MDD associated cognitive dysfunction., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

The 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. We 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 before (2014-2018) and during (April-May 2020) the first lockdown in Germany. 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. HC 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 associated with trait anxiety (STAI-T), conscientiousness (NEO-FFI), Covid-19 impact, and sex. Subjective isolation in HC was associated with social support (FSozu), Covid-19 impact, age, and sex; in patients, it was associated with social support and Covid-19 impact. Our data shed light on differential effects of the pandemic in psychiatric patients and HC. Low social support, high conscientiousness and high trait anxiety are associated with elevated distress during the pandemic. These variables might be valuable for the creation of risk profiles of Covid-19-related distress for direct translation into clinical practice., (Copyright © 2021. Published by Elsevier B.V.)

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = -0.164 to -0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = -0.173 to -0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Background: Subclinical psychotic-like experiences (PLE), resembling key symptoms of psychotic disorders, are common throughout the general population and possibly associated with psychosis risk. There is evidence that such symptoms are also associated with structural brain changes., Methods: In 672 healthy individuals, we assessed PLE and associated distress with the symptom-checklist-90R (SCL-90R) scales 'schizotypal signs' (STS) and 'schizophrenia nuclear symptoms' (SNS) and analysed associations with voxel- and surfaced-based brain structural parameters derived from structural magnetic resonance imaging at 3 T with CAT12., Results: For SNS, we found a positive correlation with the volume in the left superior parietal lobule and the precuneus, and a negative correlation with the volume in the right inferior temporal gyrus [p < 0.05 cluster-level Family Wise Error (FWE-corrected]. For STS, we found a negative correlation with the volume of the left and right precentral gyrus (p < 0.05 cluster-level FWE-corrected). Surface-based analyses did not detect any significant clusters with the chosen statistical threshold of p < 0.05. However, in exploratory analyses (p < 0.001, uncorrected), we found a positive correlation of SNS with gyrification in the left insula and rostral middle frontal gyrus and of STS with the left precuneus and insula, as well as a negative correlation of STS with gyrification in the left temporal pole., Conclusions: Our results show that brain structures in areas implicated in schizophrenia are also related to PLE and its associated distress in healthy individuals. This pattern supports a dimensional model of the neural correlates of symptoms of the psychotic spectrum.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

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., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools., Aims: To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics., Method: Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts., Results: Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = -0.34 years, s.e. = 0.08), major depression (β = -0.34 years, s.e. = 0.08), schizophrenia (β = -0.39 years, s.e. = 0.08), and educational attainment (β = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO., Conclusions: AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

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., (© 2021. The Author(s).)

Introduction: More than a century of research on the neurobiological underpinnings of major psychiatric disorders (major depressive disorder [MDD], bipolar disorder [BD], schizophrenia [SZ], and schizoaffective disorder [SZA]) has been unable to identify diagnostic markers. An alternative approach is to study dimensional psychopathological syndromes that cut across categorical diagnoses. The aim of the current study was to identify gray matter volume (GMV) correlates of transdiagnostic symptom dimensions., Methods: We tested the association of 5 psychopathological factors with GMV using multiple regression models in a sample of N = 1069 patients meeting Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for MDD (n = 818), BD (n = 132), and SZ/SZA (n = 119). T1-weighted brain images were acquired with 3-Tesla magnetic resonance imaging and preprocessed with CAT12. Interactions analyses (diagnosis × psychopathological factor) were performed to test whether local GMV associations were driven by DSM-IV diagnosis. We further tested syndrome specific regions of interest (ROIs)., Results: Whole brain analysis showed a significant negative association of the positive formal thought disorder factor with GMV in the right middle frontal gyrus, the paranoid-hallucinatory syndrome in the right fusiform, and the left middle frontal gyri. ROI analyses further showed additional negative associations, including the negative syndrome with bilateral frontal opercula, positive formal thought disorder with the left amygdala-hippocampus complex, and the paranoid-hallucinatory syndrome with the left angular gyrus. None of the GMV associations interacted with DSM-IV diagnosis., Conclusions: We found associations between psychopathological syndromes and regional GMV independent of diagnosis. Our findings open a new avenue for neurobiological research across disorders, using syndrome-based approaches rather than categorical diagnoses., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet many previous studies lack integration of different features of MDD with structural connectivity in multivariate modeling approaches. In n = 595 MDD patients, we used structural equation modeling (SEM) to test the intercorrelations between anhedonia, anxiety, neuroticism, and cognitive control in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures in association with those clinical variables, and finally integrated brain connectivity associations, clinical/cognitive variables into a multivariate SEM. We first confirmed our clinical/cognitive SEM. DTI analyses (FWE-corrected) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF). An extended SEM confirmed the associations of ATR FA with anhedonia and UF/IFOF AD with neuroticism impacting on cognitive control. Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. The multivariate approach shows feasibility of explaining heterogeneity within MDD and tracks this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

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., (© 2021. The Author(s).)