The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression.

Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Neural foundation of the diathesis-stress model: longitudinal gray matter volume changes in response to stressful life events in major depressive disorder and healthy controls.

Recurrences of depressive episodes in major depressive disorder (MDD) can be explained by the diathesis-stress model, suggesting that stressful life events (SLEs) can trigger MDD episodes in individuals with pre-existing vulnerabilities. However, the longitudinal neurobiological impact of SLEs on gray matter volume (GMV) in MDD and its interaction with early-life adversity remains unresolved. In 754 participants aged 18-65 years (362 MDD patients; 392 healthy controls; HCs), we assessed longitudinal associations between SLEs (Life Events Questionnaire) and whole-brain GMV changes (3 Tesla MRI) during a 2-year interval, using voxel-based morphometry in SPM12/CAT12. We also explored the potential moderating role of childhood maltreatment (Childhood Trauma Questionnaire) on these associations. Over the 2-year interval, HCs demonstrated significant GMV reductions in the middle frontal, precentral, and postcentral gyri in response to higher levels of SLEs, while MDD patients showed no such GMV changes. Childhood maltreatment did not moderate these associations in either group. However, MDD patients who had at least one depressive episode during the 2-year interval, compared to those who did not, or HCs, showed GMV increases in the middle frontal, precentral, and postcentral gyri associated with an increase in SLEs and childhood maltreatment. Our findings indicate distinct GMV changes in response to SLEs between MDD patients and HCs. GMV decreases in HCs may represent adaptive responses to stress, whereas GMV increases in MDD patients with both childhood maltreatment and a depressive episode during the 2-year interval may indicate maladaptive changes, suggesting a neural foundation for the diathesis-stress model in MDD recurrences.

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach.

BACKGROUND: Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression. METHODS: Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test. RESULTS: Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients. CONCLUSION: The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

Multi-modal morphometric association study of subclinical depressive symptoms using voxel-based morphometry, cortical thickness, and diffusion tensor imaging (DTI).

BACKGROUND: Case-control studies in major depression have established numerous regional grey and white matter effects in fronto-limbic brain regions. Yet, brain structural studies of dimensional depressive psychopathology within the subclinical spectrum are still limited, in particular for multi-modal imaging approaches. METHODS: Using voxel-based and surface-based morphometry (cortical thickness) in combination with diffusion tensor imaging (DTI) in a large non-clinical sample (N = 300), we correlated grey and white matter structural variation with subclinical depressive symptoms assessed with Beck's Depression inventory (BDI). RESULTS: We found a significant decrease of axial diffusivity associated with higher BDI scores in the left hippocampal part of the cingulum bundle (p < 0.05, threshold free cluster enhanced [TFCE] p-value) and some grey matter trend results e.g., a non-linear negative correlation of cortical thickness with depressive symptom load in the right pre/postcentral cortex (pFWE = 0.054, family wise error [FWE] peak level corrected) and a trend in grey matter volume decrease in women in the inferior frontal gyrus (pFWE = 0.054). LIMITATIONS: Since all grey matter effects disappear after FWE correction, we assume more stable effects in a larger, less homogenous sample enriched by help-seeking subjects covering a wider range of subclinical psychopathology. CONCLUSION: Our study adds correlations between single depressive symptoms and brain structure to a growing literature. Since subclinical depression is increasingly recognised to be relevant in our understanding of manifest depression, early detection and identification of potential brain correlates of minor depressive symptoms has the potential to expand and reveal possible biomarkers and early psychological treatment.

A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder.

Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure: Patients with MDD and healthy controls. Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

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.

Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers.

Background: Controllability is a measure of the brain's ability to orchestrate neural activity which can be quantified in terms of properties of the brain's network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging. Methods: In a sample of 814 healthy individuals (aged 33.9 ± 12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability). Results and conclusion: In nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow.

Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from  structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.

Structural connectivity of grandiose versus vulnerable narcissism as models of social dominance and subordination.

Social dominance and subordination have been linked to fronto-limbic and fronto-thalamic networks and are related to phenotypes such as grandiose vs. vulnerable narcissistic traits. The latter have been linked to clinical features such as empathy and emotional regulation. In this study we tested the hypotheses that narcissistic traits are associated with white matter integrity in fasciculus uncinate, cingulum, and anterior thalamic radiation (ATR). We applied the Pathological Narcissism Inventory (PNI) to assess narcissistic traits in a sample of 267 psychiatrically healthy individuals. We used 3 T MRI to acquire Diffusion Tensor Imaging data for analysis with TBSS in FSL applying TFCE to test for correlations of fractional anisotropy (FA) and PNI scales. We detected a significant positive correlation of PNI total and FA in the right posterior cingulum. PNI Vulnerability was significantly correlated with FA in the left anterior and right posterior cingulum. We did not find overall correlations with PNI Grandiosity, but additional analyses showed significant effects with FA of ATR. Our results strengthen network models for narcissism underlying both personality variation and pathology. Especially associations of narcissistic vulnerability within fronto-limbic tracts suggest overlaps within neural correlates of related phenotypes like neuroticism, social subordination, and negative emotionality.

Relative importance of speech and voice features in the classification of schizophrenia and depression.

Speech is a promising biomarker for schizophrenia spectrum disorder (SSD) and major depressive disorder (MDD). This proof of principle study investigates previously studied speech acoustics in combination with a novel application of voice pathology features as objective and reproducible classifiers for depression, schizophrenia, and healthy controls (HC). Speech and voice features for classification were calculated from recordings of picture descriptions from 240 speech samples (20 participants with SSD, 20 with MDD, and 20 HC each with 4 samples). Binary classification support vector machine (SVM) models classified the disorder groups and HC. For each feature, the permutation feature importance was calculated, and the top 25% most important features were used to compare differences between the disorder groups and HC including correlations between the important features and symptom severity scores. Multiple kernels for SVM were tested and the pairwise models with the best performing kernel (3-degree polynomial) were highly accurate for each classification: 0.947 for HC vs. SSD, 0.920 for HC vs. MDD, and 0.932 for SSD vs. MDD. The relatively most important features were measures of articulation coordination, number of pauses per minute, and speech variability. There were moderate correlations between important features and positive symptoms for SSD. The important features suggest that speech characteristics relating to psychomotor slowing, alogia, and flat affect differ between HC, SSD, and MDD.

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.

Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders.

Syntax, the grammatical structure of sentences, is a fundamental aspect of language. It remains debated whether reduced syntactic complexity is unique to schizophrenia spectrum disorder (SSD) or whether it is also present in major depressive disorder (MDD). Furthermore, the association of syntax (including syntactic complexity and diversity) with language-related neuropsychology and psychopathological symptoms across disorders remains unclear. Thirty-four SSD patients and thirty-eight MDD patients diagnosed according to DSM-IV-TR as well as forty healthy controls (HC) were included and tasked with describing four pictures from the Thematic Apperception Test. We analyzed the produced speech regarding its syntax delineating measures for syntactic complexity (the total number of main clauses embedding subordinate clauses) and diversity (number of different types of complex sentences). We performed cluster analysis to identify clusters based on syntax and investigated associations of syntactic, to language-related neuropsychological (verbal fluency and verbal episodic memory), and psychopathological measures (positive and negative formal thought disorder) using network analyses. Syntax in SSD was significantly reduced in comparison to MDD and HC, whereas the comparison of HC and MDD revealed no significant differences. No associations were present between speech measures and current medication, duration and severity of illness, age or sex; the single association accounted for was education. A cluster analysis resulted in four clusters with different degrees of syntax across diagnoses. Subjects with less syntax exhibited pronounced positive and negative symptoms and displayed poorer performance in executive functioning, global functioning, and verbal episodic memory. All cluster-based networks indicated varying degrees of domain-specific and cross-domain connections. Measures of syntactic complexity were closely related while syntactic diversity appeared to be a separate node outside of the syntactic network. Cross-domain associations were more salient in more complex syntactic production.

Negative Stressful Life Events and Social Support Are Associated With White Matter Integrity in Depressed Patients and Healthy Control Participants: A Diffusion Tensor Imaging Study.

BACKGROUND: Negative stressful life events and deprivation of social support play critical roles in the development and maintenance of major depressive disorder (MDD). The present study aimed to investigate in a large sample of patients with MDD and healthy control participants (HCs) whether these effects are also reflected in white matter (WM) integrity. METHODS: In this diffusion tensor imaging study, 793 patients with MDD and 793 age- and sex-matched HCs were drawn from the Marburg-Münster Affective Disorders Cohort Study (MACS) and completed the Life Events Questionnaire (LEQ) and Social Support Questionnaire (SSQ). Generalized linear models were performed to test voxelwise associations between fractional anisotropy (FA) and diagnosis (analysis 1), LEQ (analysis 2), and SSQ (analysis 3). We examined whether SSQ interacts with LEQ on FA or is independently associated with improved WM integrity (analysis 4). RESULTS: Patients with MDD showed lower FA in several frontotemporal association fibers compared with HCs (pTFCE-FWE = .028). Across both groups, LEQ correlated negatively with FA in widely distributed WM tracts (pTFCE-FWE = .023), while SSQ correlated positively with FA in the corpus callosum (pTFCE-FWE = .043). Modeling the combined association of both variables on FA revealed significant-and antagonistic-main effects of LEQ (pTFCE-FWE = .031) and SSQ (pTFCE-FWE = .037), but no interaction of SSQ × LEQ. CONCLUSIONS: Our results indicate that negative stressful life events and social support are both related to WM integrity in opposing directions. The associations did not differ between patients with MDD and HCs, suggesting more general, rather than depression-specific, mechanisms. Furthermore, social support appears to contribute to improved WM integrity independent of stressful life events.

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.

Trait schizotypy and the psychosis prodrome: Current standard assessment of extended psychosis spectrum phenotypes.

Schizotypy has become an increasingly important construct for elaborating psychotic disorders that vary along the schizophrenic spectrum. However, different schizotypy inventories vary in conceptual approach and measurement. In addition, commonly used schizotypy scales have been seen as qualitatively different from screening instruments for prodromal schizophrenia like the Prodromal Questionnaire-16 (PQ-16). Our study investigated the psychometric properties of three schizotypy questionnaires (the Schizotypal Personality Questionnaire-Brief, Oxford-Liverpool Inventory of Feelings and Experiences, and the Multidimensional Schizotypy Scale) as well as the PQ-16 in a cohort of 383 non-clinical subjects. We initially evaluated their factor structure using Principal Component Analysis (PCA) and used Confirmatory Factor Analysis (CFA) to test a newly proposed composition of factors. PCA results support a three-factor structure of schizotypy that accounts for 71 % of the total variance, but also shows cross-loadings of some schizotypy subscales. CFA of the newly composed schizotypy factors (together with an added neuroticism factor) shows good fit. Analyses including the PQ-16 indicate considerable overlap with measures of trait schizotypy, suggesting that the PQ-16 might not be quantitatively or qualitatively different from schizotypy measurements. Taken together, results indicate that there is good support for a three-factor structure of schizotypy but also that different schizotypy measurements grasp facets of schizotypy differently. This points towards the need for an integrative approach for assessing the construct of schizotypy.

Cognitive performance and brain structural connectome alterations in major depressive disorder.

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.

Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

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.

State of illness-dependent associations of neuro-cognition and psychopathological syndromes in a large transdiagnostic cohort.

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.

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).