Associations of altered leukocyte DDR1 promoter methylation and childhood trauma with bipolar disorder and suicidal behavior in euthymic patients.

Altered DNA methylation (DNAm) patterns of discoidin domain receptor 1 (DDR1) have been found in the blood and brain of patients with schizophrenia (SCZ) and the brain of patients with bipolar disorder (BD). Childhood trauma (CT) is associated with changes in DNAm that in turn are related to suicidal behavior (SB) in patients with several psychiatric disorders. Here, using MassARRAY® technology, we studied 128 patients diagnosed with BD in remission and 141 healthy controls (HCs) to compare leukocyte DDR1 promoter DNAm patterns between patients and HCs and between patients with and without SB. Additionally, we investigated whether CT was associated with DDR1 DNAm and mediated SB. We found hypermethylation at DDR1 cg19215110 and cg23953820 sites and hypomethylation at cg14279856 and cg03270204 sites in patients with BD compared to HCs. Logistic regression models showed that hypermethylation of DDR1 cg23953820 but not cg19215110 and CT were risk factors for BD, while cg14279856 and cg03270204 hypomethylation were protective factors. In patients, CT was a risk factor for SB, but DDR1 DNAm, although associated with CT, did not mediate the association of CT with SB. This is the first study demonstrating altered leukocyte DDR1 promoter DNAm in euthymic patients with BD. We conclude that altered DDR1 DNAm may be related to immune and inflammatory mechanisms and could be a potential blood biomarker for the diagnosis and stratification of psychiatric patients.

Widespread intra-axonal signal fraction abnormalities in bipolar disorder from multicompartment diffusion MRI: Sensitivity to diagnosis, association with clinical features and pharmacologic treatment.

Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA) reductions in the brain white matter of patients with bipolar disorder, questions remain regarding the specificity and sensitivity of FA abnormalities as opposed to other diffusion metrics in the disorder. We conducted a whole-brain voxel-based multicompartment diffusion MRI study on 316 participants (i.e., 158 patients and 158 matched healthy controls) employing four diffusion metrics: the mean diffusivity (MD) and FA estimated from DTI, and the intra-axonal signal fraction (IASF) and microscopic axonal parallel diffusivity (Dpar) derived from the spherical mean technique. Our findings provide novel evidence about widespread abnormalities in other diffusion metrics in BD. An extensive overlap between the FA and IASF results suggests that the lower FA in patients may be caused by a reduced intra-axonal volume fraction or a higher macromolecular content in the intra-axonal water. We also found a diffuse alteration in MD involving white and grey matter tissue and more localised changes in Dpar. A Machine Learning analysis revealed that FA, followed by IASF, were the most helpful metric for the automatic diagnosis of BD patients, reaching an accuracy of 72%. Number of mood episodes, age of onset/duration of illness, psychotic symptoms, and current treatment with lithium, antipsychotics, antidepressants, and antiepileptics were all significantly associated with microstructure abnormalities. Lithium treatment was associated with less microstructure abnormality.

In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Longitudinal brain functional changes between mania and euthymia in bipolar disorder.

OBJECTIVES: While widespread cortical and subcortical brain functional abnormalities have been found in bipolar disorder, the changes that take place between illness phases and recovery are less clearly documented. Only a small number of longitudinal studies of manic patients, in particular, have been carried out. METHODS: Twenty-six bipolar patients underwent fMRI during performance of the n-back working memory task when manic and again after recovery. Twenty-six matched healthy controls were also scanned on two occasions. Task-related activations and de-activations were examined. RESULTS: When manic, the patients showed clusters of significantly reduced activation in the left dorsolateral prefrontal cortex (DLPFC)/precentral cortex and the parietal cortex/superior precuneus bilaterally. They also showed failure of de-activation in the ventromedial frontal cortex (vmPFC). After recovery, activation in the left DLPFC/precentral cortex and in the bilateral parietal cortex/superior precuneus clusters increased significantly. However, failure of de-activation remained present in the vmPFC. CONCLUSIONS: Recovery from mania is associated with normalization of DLPFC and parietal hypoactivation, but not with vmPFC failure of de-activation, which accordingly appears to represent a trait abnormality in the disorder.

Differential failure to deactivate the default mode network in unipolar and bipolar depression.

OBJECTIVES: Neuroimaging studies have revealed evidence of brain functional abnormalities in bipolar depressive disorder (BDD) and major depressive disorder (MDD). However, few studies to date have compared these two mood disorders directly. METHODS: Matched groups of 26 BDD type I patients, 26 MDD patients and 26 healthy controls underwent functional magnetic resonance imaging (fMRI) while performing the n-back working memory task. A whole-brain ANOVA was used to compare the three groups and clusters of significant difference were examined further using region-of-interest (ROI) analysis. RESULTS: The whole-brain ANOVA revealed a single cluster of significant difference in the medial frontal cortex. The BDD and MDD patients both showed failure to deactivate in this area compared to the controls. The BDD patients showed significantly greater failure of deactivation than the MDD patients, which was not accounted for by differences in severity or chronicity of illness between them. CONCLUSIONS: Failure of deactivation, considered to reflect default mode network dysfunction, is present to a greater extent in bipolar than unipolar depression. The study of this network may be useful in the search for brain markers that distinguish the two disorders.

Brain functional changes across the different phases of bipolar disorder.

BACKGROUND: Little is known about how functional imaging changes in bipolar disorder relate to different phases of the illness. AIMS: To compare cognitive task activation in participants with bipolar disorder examined in different phases of illness. METHOD: Participants with bipolar disorder in mania (n = 38), depression (n = 38) and euthymia (n = 38), as well as healthy controls (n = 38), underwent functional magnetic resonance imaging during performance of the n-back working memory task. Activations and de-activations were compared between the bipolar subgroups and the controls, and among the bipolar subgroups. All participants were also entered into a linear mixed-effects model. RESULTS: Compared with the controls, the mania and depression subgroups, but not the euthymia subgroup, showed reduced activation in the dorsolateral prefrontal cortex, the parietal cortex and other areas. Compared with the euthymia subgroup, the mania and depression subgroups showed hypoactivation in the parietal cortex. All three bipolar subgroups showed failure of de-activation in the ventromedial frontal cortex. Linear mixed-effects modelling revealed a further cluster of reduced activation in the left dorsolateral prefrontal cortex in the patients; this was significantly more marked in the mania than in the euthymia subgroup. CONCLUSIONS: Bipolar disorder is characterised by mood state-dependent hypoactivation in the parietal cortex. Reduced dorsolateral prefrontal activation is a further feature of mania and depression, which may improve partially in euthymia. Failure of de-activation in the medial frontal cortex shows trait-like characteristics.

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

The relationship between cognition and functioning in Bipolar Disorder: An investigation using functional imaging during working memory performance.

The psychosocial functioning of individuals suffering from bipolar disorder (BD) has a significant impact on prognosis and quality of life. The aim of this study was to assess brain functional correlates of psychosocial functioning in BD individuals during the performance of a working memory task. Sixty-two subjects (31 euthymic BD individuals and 31 matched healthy controls) underwent structural and functional magnetic resonance imaging scanning while performing the 1- and 2-back versions of the n-back task (1-back and 2-back). The Functional Assessment Short Test (FAST) and its subdomains were used to assess functioning. Whole brain analysis revealed only overall activation differences between BD patients and healthy controls, but the patients showed failure of de-activation in the medial frontal cortex. Six clusters of significant inverse correlation with the FAST scores were found in the dorsolateral prefrontal cortex, the superior parietal cortex, and temporo-occipital regions bilaterally, and in the left inferior frontal cortex. Cognitive and occupational functioning were the subdomains most significantly associated with brain activation in these clusters. The results suggest that poor psychosocial functioning in BD individuals is associated with hypoactivation in a range of cortical regions, including the fronto-parietal working memory network and inferior temporo-occipital regions.

Improved estimation of the risk of manic relapse by combining clinical and brain scan data.

INTRODUCTION: Estimating the risk of manic relapse could help the psychiatrist individually adjust the treatment to the risk. Some authors have attempted to estimate this risk from baseline clinical data. Still, no studies have assessed whether the estimation could improve by adding structural magnetic resonance imaging (MRI) data. We aimed to evaluate it. MATERIAL AND METHODS: We followed a cohort of 78 patients with a manic episode without mixed symptoms (bipolar type I or schizoaffective disorder) at 2-4-6-9-12-15-18 months and up to 10 years. Within a cross-validation scheme, we created and evaluated a Cox lasso model to estimate the risk of manic relapse using both clinical and MRI data. RESULTS: The model successfully estimated the risk of manic relapse (Cox regression of the time to relapse as a function of the estimated risk: hazard ratio (HR)=2.35, p=0.027; area under the curve (AUC)=0.65, expected calibration error (ECE)<0.2). The most relevant variables included in the model were the diagnosis of schizoaffective disorder, poor impulse control, unusual thought content, and cerebellum volume decrease. The estimations were poorer when we used clinical or MRI data separately. CONCLUSION: Combining clinical and MRI data may improve the risk of manic relapse estimation after a manic episode. We provide a website that estimates the risk according to the model to facilitate replication by independent groups before translation to clinical settings.

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium.

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual's latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences.

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder.

BACKGROUND: Individuals with schizophrenia exhibit greater inter-patient variability in functional brain activity during neurocognitive task performance. Some studies have shown associations of age and illness duration with brain function; however, the association of these variables with variability in brain function activity is not known. In order to better understand the progressive effects of age and illness duration across disorders, we examined the relationship with individual variability in brain activity. METHODS: Neuroimaging and behavioural data were extracted from harmonized datasets collectively including 212 control participants, 107 individuals with bipolar disorder, and 232 individuals with schizophrenia (total n = 551). Functional activity in response to an N-back working memory task (2-back vs 1-back) was examined. Individual variability was quantified via the correlational distance of fMRI activity between participants; mean correlational distance of one participant in relation to all others was defined as a 'variability score'. RESULTS: Greater individual variability was found in the schizophrenia group compared to the bipolar disorder and control groups (p = 1.52e-09). Individual variability was significantly associated with aging (p = 0.027), however, this relationship was not different across diagnostic groups. In contrast, in the schizophrenia sample only, a longer illness duration was associated with increased variability (p = 0.027). CONCLUSION: An increase in variability was observed in the schizophrenia group related to illness duration, beyond the effects of normal aging, implying illness-related deterioration of cognitive networks. This has clinical implications for considering long-term trajectories in schizophrenia and progressive neural and cognitive decline which may be amiable to novel treatments.

Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group.

BACKGROUND: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. METHODS: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. RESULTS: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18

Epilepsy in Neurodegenerative Diseases: Related Drugs and Molecular Pathways.

Epilepsy is a chronic disease of the central nervous system characterized by an electrical imbalance in neurons. It is the second most prevalent neurological disease, with 50 million people affected around the world, and 30% of all epilepsies do not respond to available treatments. Currently, the main hypothesis about the molecular processes that trigger epileptic seizures and promote the neurotoxic effects that lead to cell death focuses on the exacerbation of the glutamate pathway and the massive influx of Ca2+ into neurons by different factors. However, other mechanisms have been proposed, and most of them have also been described in other neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, or multiple sclerosis. Interestingly, and mainly because of these common molecular links and the lack of effective treatments for these diseases, some antiseizure drugs have been investigated to evaluate their therapeutic potential in these pathologies. Therefore, in this review, we thoroughly investigate the common molecular pathways between epilepsy and the major neurodegenerative diseases, examine the incidence of epilepsy in these populations, and explore the use of current and innovative antiseizure drugs in the treatment of refractory epilepsy and other neurodegenerative diseases.

Age- and gender-related differences in brain tissue microstructure revealed by multi-component T2 relaxometry.

In spite of extensive work, inconsistent findings and lack of specificity in most neuroimaging techniques used to examine age- and gender-related patterns in brain tissue microstructure indicate the need for additional research. Here, we performed the largest Multi-component T2 relaxometry cross-sectional study to date in healthy adults (N = 145, 18-60 years). Five quantitative microstructure parameters derived from various segments of the estimated T2 spectra were evaluated, allowing a more specific interpretation of results in terms of tissue microstructure. We found similar age-related myelin water fraction (MWF) patterns in men and women but we also observed differential male related results including increased MWF content in a few white matter tracts, a faster decline with age of the intra- and extra-cellular water fraction and its T2 relaxation time (i.e. steeper age related negative slopes) and a faster increase in the free and quasi-free water fraction, spanning the whole grey matter. Such results point to a sexual dimorphism in brain tissue microstructure and suggest a lesser vulnerability to age-related changes in women.

Altered brain responses to specific negative emotions in schizophrenia.

Deficits in emotion processing are a core feature of schizophrenia, but their neurobiological bases are poorly understood. Previous research, mainly focused on emotional face processing and emotion recognition deficits, has shown controverted results. Furthermore, the use of faces has been questioned for not entailing an appropriate stimulus to study emotional processing. This highlights the importance of investigating emotional processing abnormalities using evocative stimuli. For the first time, we have studied the brain responses to scenic stimuli in patients with schizophrenia. We selected scenes from the IAPS that elicit fear, disgust, happiness, and sadness. Twenty-six patients with schizophrenia and thirty age-, sex- and premorbid IQ-matched healthy controls were included. Behavioral task results show that patients tended to misclassify disgust and sadness as fear. Brain responses in patients were different from controls in images eliciting disgust and fear. In response to disgust images, patients hyperactivated the right temporal cortex, which was not activated by the controls. With fear images, hyperactivation was observed in brain regions involved in fear processing, including midline regions from the medial frontal cortex to the anterior cingulate cortex, the superior frontal gyrus, inferior and superior temporal cortex, and visual areas. These results suggest that schizophrenia is characterized by hyper-responsivity to stimuli evoking high-arousal, negative emotions, and a bias towards fear in emotion recognition.

Genomic Characterization of Host Factors Related to SARS-CoV-2 Infection in People with Dementia and Control Populations: The GR@ACE/DEGESCO Study.

Emerging studies have suggested several chromosomal regions as potential host genetic factors involved in the susceptibility to SARS-CoV-2 infection and disease outcome. We nested a COVID-19 genome-wide association study using the GR@ACE/DEGESCO study, searching for susceptibility factors associated with COVID-19 disease. To this end, we compared 221 COVID-19 confirmed cases with 17,035 individuals in whom the COVID-19 disease status was unknown. Then, we performed a meta-analysis with the publicly available data from the COVID-19 Host Genetics Initiative. Because the APOE locus has been suggested as a potential modifier of COVID-19 disease, we added sensitivity analyses stratifying by dementia status or by disease severity. We confirmed the existence of the 3p21.31 region (LZTFL1, SLC6A20) implicated in the susceptibility to SARS-CoV-2 infection and TYK2 gene might be involved in COVID-19 severity. Nevertheless, no statistically significant association was observed in the COVID-19 fatal outcome or in the stratified analyses (dementia-only and non-dementia strata) for the APOE locus not supporting its involvement in SARS-CoV-2 pathobiology or COVID-19 prognosis.

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

IMPORTANCE: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. OBJECTIVE: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. DESIGN, SETTING, AND PARTICIPANTS: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. MAIN OUTCOMES AND MEASURES: Interregional profiles of group difference in cortical thickness between cases and controls. RESULTS: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders. CONCLUSIONS AND RELEVANCE: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.