Brain Structural Network Connectivity of Formal Thought Disorder Dimensions in Affective and Psychotic Disorders.

BACKGROUND: The psychopathological syndrome of formal thought disorder (FTD) is not only present in schizophrenia (SZ), but also highly prevalent in major depressive disorder and bipolar disorder. It remains unknown how alterations in the structural white matter connectome of the brain correlate with psychopathological FTD dimensions across affective and psychotic disorders. METHODS: Using FTD items of the Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms, we performed exploratory and confirmatory factor analyses in 864 patients with major depressive disorder (n= 689), bipolar disorder (n = 108), or SZ (n = 67) to identify psychopathological FTD dimensions. We used T1- and diffusion-weighted magnetic resonance imaging to reconstruct the structural connectome of the brain. To investigate the association of FTD subdimensions and global structural connectome measures, we employed linear regression models. We used network-based statistic to identify subnetworks of white matter fiber tracts associated with FTD symptomatology. RESULTS: Three psychopathological FTD dimensions were delineated, i.e., disorganization, emptiness, and incoherence. Disorganization and incoherence were associated with global dysconnectivity. Network-based statistics identified subnetworks associated with the FTD dimensions disorganization and emptiness but not with the FTD dimension incoherence. Post hoc analyses on subnetworks did not reveal diagnosis × FTD dimension interaction effects. Results remained stable after correcting for medication and disease severity. Confirmatory analyses showed a substantial overlap of nodes from both subnetworks with cortical brain regions previously associated with FTD in SZ. CONCLUSIONS: We demonstrated white matter subnetwork dysconnectivity in major depressive disorder, bipolar disorder, and SZ associated with FTD dimensions that predominantly comprise brain regions implicated in speech. Results open an avenue for transdiagnostic, psychopathology-informed, dimensional studies in pathogenetic research.

Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach.

BACKGROUND: Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. METHODS: In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. RESULTS: Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. CONCLUSIONS: Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.

Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Temporal stability and state-dependence of retrospective self-reports of childhood maltreatment in healthy and depressed adults.

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, R² = .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).

Associations of gestational age with gyrification and neurocognition in healthy adults.

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.

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders.

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.

Brain structural correlates of recurrence following the first episode in patients with major depressive disorder.

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.

Reduced hippocampal gray matter volume is a common feature of patients with major depression, bipolar disorder, and schizophrenia spectrum disorders.

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.

Interaction of recent stressful life events and childhood abuse on orbitofrontal grey matter volume in adults with depression.

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.

Association between stressful life events and grey matter volume in the medial prefrontal cortex: A 2-year longitudinal study.

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.

Investigating the phenotypic and genetic associations between personality traits and suicidal behavior across major mental health diagnoses.

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.

Which traits predict elevated distress during the Covid-19 pandemic? Results from a large, longitudinal cohort study with psychiatric patients and healthy controls.

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.

Genetic risk for psychiatric illness is associated with the number of hospitalizations of bipolar disorder patients.

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 (ß = 0.11, SE = 0.03, p = 1.17 × 10-3) and schizophrenia (ß = 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.

Interaction of developmental factors and ordinary stressful life events on brain structure in adults.

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.