Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders

Gray matter asymmetry alterations in children and adolescents with comorbid autism spectrum disorder and attention-deficit/hyperactivity disorder

Despite the high coexistence of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) (ASD + ADHD), the underlying neurobiological basis of this disorder remains unclear. Altered brain structural asymmetries have been verified in ASD and ADHD, respectively, making brain asymmetry a candidate for characterizing this coexisting disorder. Here, we measured the gray matter (GM) volume asymmetry in ASD + ADHD versus ASD without ADHD (ASD-only), ADHD without ASD (ADHD-only), and typically developing controls (TDc). High-resolution T1-weighted data from 48 ASD + ADHD, 63 ASD-only, 32 ADHD-only, and 211 matched TDc were included in our study. We also assessed brain-behavior relationships and the effects of age on GM asymmetry. We found that there were both shared and disorder-specific GM volume asymmetry alterations in ASD + ADHD, ASD-only, and ADHD-only compared with TDc. This finding demonstrates that ASD + ADHD is neither an endophenocopy nor an additive pathology of ASD and ADHD, but an entirely different neuroanatomical pathology. In addition, ASD + ADHD displayed altered GM volume asymmetries in the prefrontal regions responsible for executive function and theory of mind compared with ASD-only. We also found significant effects of age on GM asymmetry. The present study may provide structural insights into the neural basis of ASD + ADHD.

Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis

BACKGROUND AND HYPOTHESIS

Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease.

STUDY DESIGN

We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes.

STUDY RESULTS

All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05).

CONCLUSIONS

Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

Psychosis superspectrum II: neurobiology, treatment, and implications

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.

Contributions of Polygenic Risk and Disease Status to Gray Matter Abnormalities in Major Depression

BACKGROUND

Gray matter (GM) abnormalities in depression are potentially attributable to some combination of trait, state, and illness history factors. Here, we sought to determine the contributions of polygenic risk for depression, depressive disease status, and the interaction of these factors to these GM abnormalities.

METHODS

We conducted a cross-sectional comparison using a 2 × 3 factorial design examining effects of polygenic risk for depression (lower vs. upper quartile) and depression status (never depressed, currently depressed, or remitted depression) on regional GM concentration and GM volume. Participants were a subset of magnetic resonance imaging-scanned UK Biobank participants comprising 2682 people (876 men, 1806 women) algorithmically matched on 16 potential confounders.

RESULTS

In women but not men, we observed that elevated polygenic risk for depression was associated with reduced cerebellar GM volume. This deficit occurred in salience and dorsal attention network regions of the cerebellum and was associated with poorer performance on tests of attention and executive function but not fluid intelligence. Moreover, in women with current depression compared to both women with remitted depression and women who never had depression, we observed GM reductions in ventral and medial prefrontal, insular, and medial temporal regions. These state-related abnormalities remained when accounting for antidepressant medication status.

CONCLUSIONS

Neuroanatomical deficits attributed broadly to major depression are more likely due to an aggregation of independent factors. Polygenic risk for depression accounted for cerebellar structural abnormalities that themselves accounted for cognitive deficits observed in this disorder. Medial and ventral prefrontal, insular, and temporal cortex deficits constituted a much larger proportion of the aggregate deficit and were attributable to the depressed state.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

Neuroimaging and Biosample Collection in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data

BACKGROUND

The Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far.

METHODS

The current study includes youths (ages 11-24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia.

RESULTS

Of the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%).

CONCLUSIONS

The high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care.

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Morphometric network-based abnormalities correlate with psychiatric comorbidities and gene expression in PCDH19-related developmental and epileptic encephalopathy

Protocadherin-19 (PCDH19) developmental and epileptic encephalopathy causes an early-onset epilepsy syndrome with limbic seizures, typically occurring in clusters and variably associated with intellectual disability and a range of psychiatric disorders including hyperactive, obsessive-compulsive and autistic features. Previous quantitative neuroimaging studies revealed abnormal cortical areas in the limbic formation (parahippocampal and fusiform gyri) and underlying white-matter fibers. In this study, we adopted morphometric, network-based and multivariate statistical methods to examine the cortex and substructure of the hippocampus and amygdala in a cohort of 20 PCDH19-mutated patients and evaluated the relation between structural patterns and clinical variables at individual level. We also correlated morphometric alterations with known patterns of PCDH19 expression levels. We found patients to exhibit high-significant reductions of cortical surface area at a whole-brain level (left/right pvalue = 0.045/0.084), and particularly in the regions of the limbic network (left/right parahippocampal gyri pvalue = 0.230/0.016; left/right entorhinal gyri pvalue = 0.002/0.327), and bilateral atrophy of several subunits of the amygdala and hippocampus, particularly in the CA regions (head of the left CA3 pvalue = 0.002; body of the right CA3 pvalue = 0.004), and differences in the shape of hippocampal structures. More severe psychiatric comorbidities correlated with more significant altered patterns, with the entorhinal gyrus (pvalue = 0.013) and body of hippocampus (pvalue = 0.048) being more severely affected. Morphometric alterations correlated significantly with the known expression patterns of PCDH19 (rvalue = -0.26, pspin = 0.092). PCDH19 encephalopathy represents a model of genetically determined neural network based neuropsychiatric disease in which quantitative MRI-based findings correlate with the severity of clinical manifestations and had have a potential predictive value if analyzed early.

Reward Functioning in General and Specific Psychopathology in Children and Adults

OBJECTIVE

Problems with reward processing have been implicated in multiple psychiatric disorders, but psychiatric comorbidities are common and their specificity to individual psychopathologies is unknown. Here, we evaluate the association between reward functioning and general or specific psychopathologies.

METHOD

1,213 adults and their1,531 children (ages 6-12) completed various measures of the Positive Valence System domain from the Research Domain Criteria (RDoC). Psychopathology was assessed using the Child Behavior Checklist for children and the Adult Self Report for parents.

RESULTS

One general factor identified via principal factors factor analysis explained most variance in psychopathology in both groups. Measures of reward were associated with the general factor and most specific psychopathologies. Certain reward constructs were associated solely with specific psychopathologies but not general psychopathology. However, some prior associations between reward and psychopathology did not hold following removal of comorbidity.

CONCLUSION

Reward dysfunction is significantly associated with both general and specific psychopathologies.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference

BACKGROUND

Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies.

METHODS

Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD.

RESULTS

We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network.

CONCLUSIONS

The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.

Causal associations between blood lipids and brain structures: a Mendelian randomization study

The potential causal association between dyslipidemia and brain structures remains unclear. Therefore, this study aimed to investigate whether circulating lipids are causally associated with brain structure alterations using Mendelian randomization analysis. Genome-wide association study summary statistics of blood lipids and brain structures were obtained from publicly available databases. Inverse-variance weighted method was used as the primary method to assess causality. In addition, four additional Mendelian randomization methods (MR-Egger, weighted median, simple mode, and weighted mode) were applied to supplement inverse-variance weighted. Furthermore, Cochrane's Q test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis were performed for sensitivity analyses. After Bonferroni corrections, two causal associations were finally identified: elevated non-high-density lipoprotein cholesterol level leads to higher average cortical thickness (β = 0.0066 mm, 95% confidence interval: 0.0045-0.0087 mm, P = 0.001); and elevated high-density lipoprotein cholesterol level leads to higher inferior temporal surface area (β = 18.6077 mm2, 95% confidence interval: 11.9835-25.2320 mm2, P = 0.005). Four additional Mendelian randomization methods indicated parallel results. Sensitivity tests demonstrated the stability. Overall, the present study showed causal relationships between several lipid profiles and specific brain structures, providing new insights into the link between dyslipidemia and neurological disorders.

Unravelling the liver-brain connection: A two-sample Mendelian randomization study investigating the causal relationship between NAFLD and cortical structure

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) has been linked to cognitive decline and neuropsychiatric conditions, implying a potential connection between NAFLD and brain health. However, the causal association between NAFLD and cortical changes remains uncertain. This study aimed to examine the causal impact of NAFLD on cortical structures using a two-sample Mendelian randomization (MR) approach.

METHODS

Summary data from genome-wide association studies (GWAS) for NAFLD were gathered from large-scale cohorts. Surface area (SA) and cortical thickness (TH) measurements were derived from Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium magnetic resonance imaging (MRI) data of 33,992 participants. Inverse-variance weighted (IVW) served as the primary method. Additional sensitivity analyses, including MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), MR-Egger, and weighted median procedures, were conducted to detect heterogeneity and pleiotropy.

RESULTS

Our MR analysis revealed that NAFLD led to notable alterations in cortical structures, particularly in the pars orbitalis gyrus. Specifically, genetically predicted NAFLD was linked to a decrease in TH (β = -0.008 mm, 95 % CI: -0.013 mm to -0.004 mm, P = 3.00 × 10-4) within this region. No significant heterogeneity and pleiotropy were identified.

CONCLUSION

The two-sample MR study supports the existence of a liver-brain axis by demonstrating a causal association between NAFLD and changes in cortical structures. These findings emphasize the potential association between NAFLD and brain health, which could have implications for preventing and treating cognitive deficits and neuropsychiatric conditions in patients with NAFLD.

Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

Factors related to the severity of obsessive-compulsive symptoms and their impact on suicide risk in epileptic patients

OBJECTIVE

To explore relevant factors for the severity of obsessive-compulsive symptoms (OCSs) in adult epileptic patients and investigate whether the severity of OCSs is a mediator in the relationship between depressive/anxiety symptoms and suicide risk in epileptic patients.

METHODS

This was a cross-sectional study from a hospital in Northeast China. Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Neurological Disorders Depression Inventory for Epilepsy (NDDIE), Generalized Anxiety Disorder (GAD-7), and Nurses' Global Assessment of Suicide Risk (NGASR) were used to assess the severity of OCSs, depressive symptoms, anxiety symptoms, and suicide risk in epileptic patients, respectively. The independent factors of the severity of OCSs and their mediating effects in the relationship between depressive/anxiety symptoms and suicide risk were evaluated by regression analyses and mediator models, respectively.

RESULTS

NDDIE scores (β = 0.404, p < 0.001), GAD-7 scores (β = 0.247, p = 0.009), and polytherapy (β = 0.119, p = 0.032) were the independent factors of Y-BOCS scores. The Y-BOCS scores partially mediated the relationship between GAD-7 scores and NGASR scores (standardized coefficients of indirect effect = 0.109, Bootstrap 95% CI = 0.024 to 0.214). Still, they did not mediate the relationship between NDDIE scores and NGASR scores (standardized coefficients of indirect effect = 0.062, Bootstrap 95% CI = -0.024 to 0.169).

CONCLUSIONS

Depressive symptoms, anxiety symptoms, and polytherapy are independently associated with the severity of OCSs in epileptic patients. Depressive and anxiety symptoms mediate the effect of the severity of OCSs on suicide risk in epileptic patients completely.

Replicable brain-phenotype associations require large-scale neuroimaging data

Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.

Graph Self-Supervised Learning With Application to Brain Networks Analysis

The less training data and insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is significant to construct a learning framework that can capture more information in limited data and insufficient supervision. To address these issues, we focus on self-supervised learning and aim to generalize the self-supervised learning to the brain networks, which are non-Euclidean graph data. More specifically, we propose an ensemble masked graph self-supervised framework named BrainGSLs, which incorporates 1) a local topological-aware encoder that takes the partially visible nodes as input and learns these latent representations, 2) a node-edge bi-decoder that reconstructs the masked edges by the representations of both the masked and visible nodes, 3) a signal representation learning module for capturing temporal representations from BOLD signals and 4) a classifier used for the classification. We evaluate our model on three real medical clinical applications: diagnosis of Autism Spectrum Disorder (ASD), diagnosis of Bipolar Disorder (BD) and diagnosis of Major Depressive Disorder (MDD). The results suggest that the proposed self-supervised training has led to remarkable improvement and outperforms state-of-the-art methods. Moreover, our method is able to identify the biomarkers associated with the diseases, which is consistent with the previous studies. We also explore the correlation of these three diseases and find the strong association between ASD and BD. To the best of our knowledge, our work is the first attempt of applying the idea of self-supervised learning with masked autoencoder on the brain network analysis.

Anxiety, mood, and substance use disorders in adult men and women with and without Attention-Deficit/Hyperactivity Disorder: a substantive and methodological overview

Knowledge on psychiatric comorbidity in adult ADHD is essential for prevention, detection, and treatment of these conditions. This review (1) focuses on large studies (n> 10,000; surveys, claims data, population registries) to identify (a) overall, (b) sex- and (c) age-specific patterns of comorbidity of anxiety disorders (ADs), major depressive disorder (MDD), bipolar disorder (BD) and substance use disorders (SUDs) in adults with ADHD relative to adults without ADHD; and (2) describes methodological challenges relating to establishing comorbidity in ADHD in adults as well as priorities for future research. Meta-analyses (ADHD: n=550,748; no ADHD n=14,546,814) yielded pooled odds ratios of 5.0(CI:3.29-7.46) for AD, 4.5(CI:2.44-8.34) for MDD, 8.7(CI:5.47-13.89) for BD and 4.6(CI:2.72-7.80) for SUDs, indicating strong differences in adults with compared to adults without ADHD. Moderation by sex was not found: high comorbidity held for both men and women with sex-specific patterns as in the general population: higher prevalences of ADs, MDD and BD in women and a higher prevalence of SUDs in men. Insufficient data on different phases of the adult lifespan prevented conclusions on developmental changes in comorbidity. We discuss methodological challenges, knowledge gaps, and future research priorities.

Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions

Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Considerations toward an epigenetic and common pathways theory of mental disorder

Psychopathology emerges from the dynamic interplay of physiological and mental processes and ecological context. It can be seen as a failure of recursive, homeostatic processes to achieve adaptive re-equilibrium. This general statement can be actualized with consideration of polygenic liability, early exposures, and multiunit (multi-"level") analysis of the psychological action and the associated physiological and neural operations, all in the context of the developmental exposome. This article begins by identifying key principles and clarifying key terms necessary to mental disorder theory. It then ventures a sketch of a model that highlights epigenetic dynamics and proposes a common pathways hypothesis toward psychopathology. An epigenetic perspective elevates the importance of developmental context and adaptive systems, particularly in early life, while opening the door to new mechanistic discovery. The key proposal is that a finite number of homeostatic biological and psychological mechanisms are shared across most risky environments (and possibly many genetic liabilities) for psychopathology. Perturbation of these mediating mechanisms leads to development of psychopathology. A focus on dynamic changes in these homeostatic mechanisms across multiple units of analysis and time points can render the problem of explaining psychopathology tractable. Key questions include the mapping of recursive processes over time, at adequate density, as mental disorders unfold across development. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cortical profiles of numerous psychiatric disorders and normal development share a common pattern

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder

Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.

Cumulative Impact of Morphometric Features in Schizophrenia in Two Independent Samples

Schizophrenia and bipolar disorder share a common structural brain alteration profile. However, there is considerable between- and within-diagnosis variability in these features, which may underestimate informative individual differences. Using a recently established morphometric risk score (MRS) approach, we aim to provide confirmation that individual MRS scores are higher in individuals with a psychosis diagnosis, helping to parse individual heterogeneity. Using the Human Connectome Project Early Psychosis (N = 124), we estimate MRS for psychosis and specifically for bipolar/schizophrenia using T1-weighted MRI data and prior meta-analysis effect sizes. We confirm associations in an independent replication sample (N = 69). We assess (1) the impact of diagnosis on these MRS, (2) compare effect sizes of MRS to all individual, cytoarchitecturally defined brain regions, and (3) perform negative control analyses to assess MRS specificity. The MRS specifically for SCZ was higher in the whole psychosis group (Cohen's d = 0.56; P = 0.003) and outperformed any single region of interest in standardized mean difference (ZMRS>75 ROIS = 2.597; P = 0.009) and correlated with previously reported effect sizes (PSPIN/SHUFFLE < 0.005). MRS without Enhancing Neuroimaging Genomics through Meta-Analysis weights did not delineate groups with empirically null associations (t = 2.29; P = 0.02). We replicate MRS specifically for SCZ associations in the independent sample. Akin to polygenic risk scoring and individual allele effect sizes, these observations suggest that assessing the combined impact of regional structural alterations may be more informative than any single cytoarchitecturally constrained cortical region, where well-powered, meta-analytical samples are informative in the delineation of diagnosis and within psychosis case differences, in smaller independent samples.

Whole exome sequencing in dense families suggests genetic pleiotropy amongst Mendelian and complex neuropsychiatric syndromes

Whole Exome Sequencing (WES) studies provide important insights into the genetic architecture of serious mental illness (SMI). Genes that are central to the shared biology of SMIs may be identified by WES in families with multiple affected individuals with diverse SMI (F-SMI). We performed WES in 220 individuals from 75 F-SMI families and 60 unrelated controls. Within pedigree prioritization employed criteria of rarity, functional consequence, and sharing by ≥ 3 affected members. Across the sample, gene and gene-set-wide case-control association analysis was performed with Sequence Kernel Association Test (SKAT). In 14/16 families with ≥ 3 sequenced affected individuals, we identified a total of 78 rare predicted deleterious variants in 78 unique genes shared by ≥ 3 members with SMI. Twenty (25%) genes were implicated in monogenic CNS syndromes in OMIM (OMIM-CNS), a fraction that is a significant overrepresentation (Fisher's Exact test OR = 2.47, p = 0.001). In gene-set SKAT, statistically significant association was noted for OMIM-CNS gene-set (SKAT-p = 0.005) but not the synaptic gene-set (SKAT-p = 0.17). In this WES study in F-SMI, we identify private, rare, protein altering variants in genes previously implicated in Mendelian neuropsychiatric syndromes; suggesting pleiotropic influences in neurodevelopment between complex and Mendelian syndromes.

Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Cross-Disorder Analysis of Shared Genetic Components Between Cortical Structures and Major Psychiatric Disorders

BACKGROUND AND HYPOTHESIS

Although large-scale neuroimaging studies have demonstrated similar patterns of structural brain abnormalities across major psychiatric disorders, the underlying genetic etiology behind these similar cross-disorder patterns is not well understood.

STUDY DESIGN

We quantified the extent of shared genetic components between cortical structures and major psychiatric disorders (CS-MPD) by using genome-wide association study (GWAS) summary statistics of 70 cortical structures (surface area and thickness of the whole cortex and 34 cortical regions) and five major psychiatric disorders, consisting of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ). Cross-disorder analyses were then conducted to estimate the degree of similarity in CS-MPD shared genetic components among these disorders.

STUDY RESULTS

The CS-MPD shared genetic components have medium-to-strong positive correlations in ADHD, BD, MDD, and SCZ (r = 0.415 to r = 0.806) while ASD was significantly correlated with ADHD, BD, and SCZ (r = 0.388 to r = 0.403). These pairwise correlations of CS-MPD shared genetic components among disorders were significantly associated with corresponding cross-disorder similarities in cortical structural abnormalities (r = 0.668), accounting for 44% variance. In addition, one latent shared factor consisted primarily of BD, MDD, and SCZ, explaining 62.47% of the total variance in CS-MPD shared genetic components of all disorders.

CONCLUSIONS

The current results bridge the gap between shared cross-disorder heritability and shared structural brain abnormalities in major psychiatric disorders, providing important implications for a shared genetic basis of cortical structures in these disorders.

Hippocampal circuit dysfunction in psychosis

Despite strong evidence of the neurodevelopmental origins of psychosis, current pharmacological treatment is not usually initiated until after a clinical diagnosis is made, and is focussed on antagonising striatal dopamine receptors. These drugs are only partially effective, have serious side effects, fail to alleviate the negative and cognitive symptoms of the disorder, and are not useful as a preventive treatment. In recent years, attention has turned to upstream brain regions that regulate striatal dopamine function, such as the hippocampus. This review draws together these recent data to discuss why the hippocampus may be especially vulnerable in the pathophysiology of psychosis. First, we describe the neurodevelopmental trajectory of the hippocampus and its susceptibility to dysfunction, exploring this region's proneness to structural and functional imbalances, metabolic pressures, and oxidative stress. We then examine mechanisms of hippocampal dysfunction in psychosis and in individuals at high-risk for psychosis and discuss how and when hippocampal abnormalities may be targeted in these groups. We conclude with future directions for prospective studies to unlock the discovery of novel therapeutic strategies targeting hippocampal circuit imbalances to prevent or delay the onset of psychosis.

Gray matter volumetric correlates of attention deficit and hyperactivity traits in emerging adolescents

Previous research has demonstrated reduction in cortical and subcortical, including basal ganglia (BG), gray matter volumes (GMV) in individuals with attention deficit hyperactivity disorder (ADHD), a neurodevelopmental condition that is more prevalent in males than in females. However, the volumetric deficits vary across studies. Whether volumetric reductions are more significant in males than females; to what extent these neural markers are heritable and relate to cognitive dysfunction in ADHD remain unclear. To address these questions, we followed published routines and performed voxel-based morphometry analysis of a data set (n = 11,502; 5,464 girls, 9–10 years) curated from the Adolescent Brain Cognition Development project, a population-based study of typically developing children. Of the sample, 634 and 2,826 were identified as monozygotic twins and dizygotic twins/siblings, respectively. In linear regressions, a cluster in the hypothalamus showed larger GMV, and bilateral caudate and putamen, lateral orbitofrontal and occipital cortex showed smaller GMVs, in correlation with higher ADHD scores in girls and boys combined. When examined separately, boys relative to girls showed more widespread (including BG) and stronger associations between GMV deficits and ADHD scores. ADHD traits and the volumetric correlates demonstrated heritability estimates (a²) between 0.59 and 0.79, replicating prior findings of the genetic basis of ADHD. Further, ADHD traits and the volumetric correlates (except for the hypothalamus) were each negatively and positively correlated with N-back performance. Together, these findings confirm volumetric deficits in children with more prominent ADHD traits. Highly heritable in both girls and boys and potentially more significant in boys than in girls, the structural deficits underlie diminished capacity in working memory and potentially other cognitive deficits in ADHD.

Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder

Gene expression covaries with brain activity as measured by resting state functional magnetic resonance imaging (MRI). However, it is unclear how genomic differences driven by disease state can affect this relationship. Here, we integrate from the ABIDE I and II imaging cohorts with datasets of gene expression in brains of neurotypical individuals and individuals with autism spectrum disorder (ASD) with regionally matched brain activity measurements from fMRI datasets. We identify genes linked with brain activity whose association is disrupted in ASD. We identified a subset of genes that showed a differential developmental trajectory in individuals with ASD compared with controls. These genes are enriched in voltage-gated ion channels and inhibitory neurons, pointing to excitation-inhibition imbalance in ASD. We further assessed differences at the regional level showing that the primary visual cortex is the most affected region in ASD. Our results link disrupted brain expression patterns of individuals with ASD to brain activity and show developmental, cell type, and regional enrichment of activity linked genes.

Gene expression patterns have been associated with functional activity patterns in the brain. Here the authors determine how gene expression patterns in the human brain supports brain phenotypes obtained from resting state fMRI imaging, identifying brain regions and genes relevant to autism.

Overlap of Neuroanatomical Involvement in Frontotemporal Dementia and Primary Psychiatric Disorders: A Meta-analysis

BACKGROUND

Despite significant symptomatic overlap between behavioral variant frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPDs), a potential overlap in their structural anatomical changes has not been studied systematically.

METHODS

In this magnetic resonance imaging-based meta-analysis, we included studies on bvFTD, schizophrenia, bipolar disorder, and autism spectrum disorder that 1) used voxel-based morphometry analysis to assess regional gray matter volumes (GMVs) and 2) reported the coordinates of the regional GMV. Separate analyses were performed comparing clusters of coordinate-based changes in the GMVs (n = 24,183) between patients and control subjects, and overlapping brain regions between bvFTD and each PPD were examined.

RESULTS

We found that GMV alterations in the prefrontal and anterior cingulate cortices, temporal lobe, amygdala, and insula comprise the transdiagnostic brain alterations in bvFTD and PPD.

CONCLUSIONS

Our meta-analysis revealed significant anatomical overlap that paves the way for future investigations of shared pathophysiological pathways, and our cross-disorder approach would provide new insights to better understand the relationship between bvFTD and PPD.

Multiscale neural signatures of major depressive, anxiety, and stress-related disorders

The extent of shared and distinct neural mechanisms underlying major depressive disorder (MDD), anxiety, and stress-related disorders is still unclear. We compared the neural signatures of these disorders in 5,405 UK Biobank patients and 21,727 healthy controls. We found the greatest case–control differences in resting-state functional connectivity and cortical thickness in MDD, followed by anxiety and stress-related disorders. Neural signatures for MDD and anxiety disorders were highly concordant, whereas stress-related disorders showed a distinct pattern. Controlling for cross-disorder genetic risk somewhat decreased the similarity between functional neural signatures of stress-related disorders and both MDD and anxiety disorders. Among cases and healthy controls, reduced within-network and increased between-network frontoparietal and default mode connectivity were associated with poorer cognitive performance (processing speed, attention, associative learning, and fluid intelligence). These results provide evidence for distinct neural circuit function impairments in MDD and anxiety disorders compared to stress disorders, yet cognitive impairment appears unrelated to diagnosis and varies with circuit function.

Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

BACKGROUND

Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ.

METHODS

We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI.

RESULTS

Both Sherlock (P = 3. 38 × 10^-6) and SMR (P = 1. 90 × 10^-8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus.

CONCLUSIONS

We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.

Genomic and neuroimaging approaches to bipolar disorder

BACKGROUND

To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder.

AIMS

To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies.

METHOD

We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings.

RESULTS

ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder.

CONCLUSIONS

The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder.

Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities

Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems in adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression, or substance use disorder (SUD). Decreases in cortical volume and thickness have also been reported in depression, SUD, and obesity, but it is unclear whether structural brain alterations represent unique disorder-specific profiles. A transdiagnostic exploration of ADHD and typical comorbid disorders could help to understand whether specific morphometric brain changes are due to ADHD or, alternatively, to the comorbid disorders. In the current study, we studied the brain morphometry of 136 subjects with ADHD with and without comorbid depression, SUD, and obesity to test whether there are unique or common brain alterations. We employed a machine-learning-algorithm trained to classify subjects with ADHD in the large ENIGMA-ADHD dataset and used it to predict the diagnostic status of subjects with ADHD and/or comorbidities. The parcellation analysis demonstrated decreased cortical thickness in medial prefrontal areas that was associated with presence of any comorbidity. However, these results did not survive correction for multiple comparisons. Similarly, the machine learning analysis indicated that the predictive algorithm grouped most of our ADHD participants as belonging to the ADHD-group, but no systematic differences between comorbidity status came up. In sum, neither a classical comparison of segmented structural brain metrics nor an ML model based on the ADHD ENIGMA data differentiate between ADHD with and without comorbidities. As the ML model is based in part on adolescent brains, this might indicate that comorbid disorders and their brain changes are not captured by the ML model because it represents a different developmental brain trajectory.

Vortioxetine Modulates the Regional Signal in First-Episode Drug-Free Major Depressive Disorder at Rest

BACKGROUND

Previous studies on brain functional alterations associated with antidepressants for major depressive disorder (MDD) have produced conflicting results because they involved short treatment periods and a variety of compounds.

METHODS

Resting-state functional magnetic resonance imaging scans were obtained from 25 first-episode drug-free patients with MDD and 25 healthy controls. The patients, who were treated with vortioxetine for 8 weeks, were scanned at two-time points (baseline and week 8 of treatment). The amplitude of low-frequency fluctuation (ALFF) in the imaging data was used to analyze local brain signal alterations associated with antidepressant treatment.

RESULTS

Compared with the controls, the patients at baseline showed decreased ALFF values in the right inferior temporal gyrus and increased ALFF values in the left inferior cerebellum, right cingulate gyrus and postcentral gyrus. After 8 weeks of vortioxetine treatment, patients showed increased ALFF values in the bilateral cingulate gyrus, middle temporal gyrus, medial superior frontal gyrus, and inferior cerebellum.

CONCLUSION

This study provided evidence that vortioxetine modulates brain signals in MDD sufferers. These findings contribute to the understanding of how antidepressants effect brain function.

Understanding Anhedonia from a Genomic Perspective

Anhedonia, or the decreased ability to experience pleasure, is a cardinal symptom of major depression that commonly occurs within other forms of psychopathology. Supportive of long-held theory that anhedonia represents a genetically influenced vulnerability marker for depression, evidence from twin studies suggests that it is moderately-largely heritable. However, the genomic sources of this heritability are just beginning to be understood. In this review, we survey what is known about the genomic architecture underlying anhedonia and related constructs. We briefly review twin and initial candidate gene studies before focusing on genome-wide association study (GWAS) and polygenic efforts. As large samples are needed to reliably detect the small effects that typically characterize common genetic variants, the study of anhedonia and related phenotypes conflicts with current genomic research requirements and frameworks that prioritize sample size over precise phenotyping. This has resulted in few and underpowered studies of anhedonia-related constructs that have largely failed to reliably identify individual variants. Nonetheless, the polygenic architecture of anhedonia-related constructs identified in these studies has genetic overlap with depression and schizophrenia as well as related brain structure (e.g., striatal volume), providing important clues to etiology that may usefully guide refinement in nosology. As we await the accumulation of larger samples for more well-powered GWAS of reward-related constructs, novel analytic techniques that leverage GWAS summary statistics (e.g., genomic structural equation modeling) may currently be used to help characterize how the genomic architecture of anhedonia is shared and distinct from that underlying other constructs (e.g., depression, neuroticism, anxiety).

Comorbidities in Obsessive-Compulsive Disorder Across the Lifespan: A Systematic Review and Meta-Analysis

Comorbidities are seen with obsessive-compulsive disorder (OCD) across the lifespan. Neurodevelopmental comorbidities are common in young children, followed by mood, anxiety, and obsessive-compulsive related disorders (OCRDs) in children, adolescents and adults, and neurological and degenerative disorders in the elderly. Understanding comorbidity prevalence and patterns has clinical and research implications. We conducted a systematic review and meta-analysis on comorbidities in OCD across the lifespan, with the objective to, first, estimate age-wise pattern and prevalence of comorbidities with OCD and, second, to examine associations of demographic (age at assessment, gender distribution) and clinical characteristics (age of onset, illness severity) with comorbidities. Four electronic databases (PubMed, EMBASE, SCOPUS, and PsycINFO) were searched using predefined search terms for articles published between 1979 and 2020. Eligible studies, across age, reported original findings on comorbidities and had an OCD sample size of ≥100. We excluded studies that did not use standardised diagnostic assessments, or that excluded patients on the basis of comorbidity. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review protocol has been registered on the International Prospective Register of Systematic Reviews. A comorbidity rate of 69% was found in a pooled sample of more than 15,000 individuals. Mood disorders (major depressive disorder), anxiety disorders (generalised anxiety disorder), neurodevelopmental disorders (NDDs) and OCRDs were the commonest comorbidities. Anxiety disorders prevailed in children, mood disorders in adults, whereas NDDs were similarly prevalent. Higher comorbidity with any psychiatric illness, NDDs, and severe mental disorders was seen in males, vs. females. Illness severity was inversely associated with rates for panic disorder, tic disorders, OCRDs, obsessive compulsive personality disorder, and anorexia nervosa. This systematic review and meta-analysis provides base rates for comorbidities in OCD across the lifespan. This has implications for comprehensive clinical evaluation and management planning. The high variability in comorbidity rates suggests the need for quality, multi-centric, large studies, using prospective designs. Systematic Review Registration: Unique Identifier: CRD42020215904.

Doxycycline exposure during adolescence and future risk of non-affective psychosis and bipolar disorder: a total population cohort study

Doxycycline has been hypothesized to prevent development of severe mental illness (SMI) through the suppression of microglia, especially if administered during the intense synaptic pruning period of adolescence. However, results from register studies on potential benefits differ considerably. The aim of the present study was to determine whether doxycycline exposure during adolescence is associated with reduced SMI risk, and to investigate if a direct and specific causality is plausible. This is a Swedish national population register-based cohort study of all individuals born from 1993 to 1997, followed from the age of 13 until end of study at the end of 2016. The primary exposure was cumulative doxycycline prescription ≥3000 mg and outcomes were first diagnosis of non-affective psychosis (F20-F29) and first diagnosis of bipolar disorder (F30-F31). Causal effects were explored through Cox regressions with relevant covariates and secondary analyses of multilevel exposure and comparison to other antibiotics. We found no association between doxycycline exposure and risk of subsequent non-affective psychosis (adjusted hazard ratio (HR) 1.15, 95% CI 0.73-1.81, p = 0.541) and an increased risk of subsequent bipolar disorder (adjusted HR 1.95, 95% CI 1.49-2.55, p < 0.001). We do not believe the association between doxycycline and bipolar disorder is causal as similar associations were observed for other common antibiotics.

Comorbidity of ADHD and adult bipolar disorder: A systematic review and meta-analysis

Attention-deficit / hyperactivity disorder (ADHD) and Bipolar Disorder (BD) are common mental disorders with a high degree of comorbidity. However, no systematic review with meta-analysis has aimed to quantify the degree of comorbidity between both disorders. To this end we performed a systematic search of the literature in October 2020. In a meta-analysis of 71 studies with 646,766 participants from 18 countries, it was found that about one in thirteen adults with ADHD was also diagnosed with BD (7.95 %; 95 % CI: 5.31-11.06), and nearly one in six adults with BD had ADHD (17.11 %; 95 % CI: 13.05-21.59 %). Substantial heterogeneity of comorbidity rates was present, highlighting the importance of contextual factors: Heterogeneity could partially be explained by diagnostic system, sample size and geographical location. Age of BD onset occurred earlier in patients with comorbid ADHD (3.96 years; 95 % CI: 2.65-5.26, p < 0.001). Cultural and methodological differences deserve attention for evaluating diagnostic criteria and clinicians should be aware of the high comorbidity rates to prevent misdiagnosis and provide optimal care for both disorders.