Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes.

BACKGROUND AND HYPOTHESIS: The gut-brain axis plays important roles in both gastrointestinal diseases (GI diseases) and schizophrenia (SCZ). Moreover, both GI diseases and SCZ exhibit notable abnormalities in brain subcortical volumes. However, the genetic mechanisms underlying the comorbidity of these diseases and the shared alterations in brain subcortical volumes remain unclear. STUDY DESIGN: Using the genome-wide association studies data of SCZ, 14 brain subcortical volumes, and 8 GI diseases, the global polygenic overlap and local genetic correlations were identified, as well as the shared genetic variants among those phenotypes. Furthermore, we conducted multi-trait colocalization analyses to bolster our findings. Functional annotations, cell-type enrichment, and protein-protein interaction (PPI) analyses were carried out to reveal the critical etiology and pathology mechanisms. STUDY RESULTS: The global polygenic overlap and local genetic correlations informed the close relationships between SCZ and both GI diseases and brain subcortical volumes. Moreover, 84 unique lead-shared variants were identified. The associated genes were linked to vital biological processes within the immune system. Additionally, significant correlations were observed with key immune cells and the PPI analysis identified several histone-associated hub genes. These findings highlighted the pivotal roles played by the immune system for both SCZ and GI diseases, along with the shared alterations in brain subcortical volumes. CONCLUSIONS: These findings revealed the shared genetic architecture contributing to SCZ and GI diseases, as well as their shared alterations in brain subcortical volumes. These insights have substantial implications for the concurrent development of intervention and therapy targets for these diseases.

Exploring functional dysconnectivity in schizophrenia: alterations in eigenvector centrality mapping and insights into related genes from transcriptional profiles.

Schizophrenia is a mental health disorder characterized by functional dysconnectivity. Eigenvector centrality mapping (ECM) has been employed to investigate alterations in functional connectivity in schizophrenia, yet the results lack consistency, and the genetic mechanisms underlying these changes remain unclear. In this study, whole-brain voxel-wise ECM analyses were conducted on resting-state functional magnetic resonance imaging data. A cohort of 91 patients with schizophrenia and 91 matched healthy controls were included during the discovery stage. Additionally, in the replication stage, 153 individuals with schizophrenia and 182 healthy individuals participated. Subsequently, a comprehensive analysis was performed using an independent transcriptional database derived from six postmortem healthy adult brains to explore potential genetic factors influencing the observed functional dysconnectivity, and to investigate the roles of identified genes in neural processes and pathways. The results revealed significant and reliable alterations in the ECM across multiple brain regions in schizophrenia. Specifically, there was a significant decrease in ECM in the bilateral superior and middle temporal gyrus, and an increase in the bilateral thalamus in both the discovery and replication stages. Furthermore, transcriptional analysis revealed 420 genes whose expression patterns were related to changes in ECM, and these genes were enriched mainly in biological processes associated with synaptic signaling and transmission. Together, this study enhances our knowledge of the neural processes and pathways involved in schizophrenia, shedding light on the genetic factors that may be linked to functional dysconnectivity in this disorder.

Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression.

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

Schizophrenia mediating the effect of smoking phenotypes on antisocial behavior: A Mendelian randomization analysis.

AIMS: Previous studies have indicated that smoking is linked to an increased risk of developing schizophrenia, and that individuals with schizophrenia are more prone to engaging in antisocial behavior. However, the causal effects of smoking behaviors on antisocial behavior and the potential mediating role of schizophrenia remains largely unclear. METHODS: In the present study, using the summary data from genome wide association studies of smoking phenotypes (N = 323,386-805,431), schizophrenia (Ncases = 53,386, Ncontrols = 77,258), and antisocial behavior (N = 85,359), we assessed bidirectional causality between smoking phenotypes and schizophrenia by the Mendelian randomization (MR) approach. Using a two-step MR approach, we further examined whether causal effects of smoking phenotypes/schizophrenia on antisocial behavior were mediated by schizophrenia/smoking phenotypes. RESULTS: The results showed that smoking initiation (SmkInit) and age of smoking initiation (AgeSmk) causally increase the risk of schizophrenia (SmkInit: OR = 2.06, 95% CI = 1.77-2.39, p = 4.36 × 10-21 ; AgeSmk: OR = 0.32, 95% CI = 0.16-0.62, p = 8.11 × 10-4 , Bonferroni corrected). However, there was no causal effect that liability to schizophrenia leads to smoking phenotypes. MR evidence also revealed causal influences of SmkInit and the amount smoked (CigDay) on antisocial behavior (SmkInit: OR = 1.28, 95% CI = 1.17-1.41, p = 2.53 × 10-7 ; CigDay: OR = 1.16, 95% CI = 1.06-1.27, p = 1.60 × 10-3 , Bonferroni corrected). Furthermore, the mediation analysis indicated that the relationship between SmkInit and antisocial behavior was partly mediated by schizophrenia (mediated proportion = 6.92%, 95% CI = 0.004-0.03, p = 9.66 × 10-3 ). CONCLUSIONS: These results provide compelling evidence for taking smoking interventions as a prevention strategy for schizophrenia and its related antisocial behavior.

Transcriptional signatures of the whole-brain voxel-wise resting-state functional network centrality alterations in schizophrenia.

Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.

Identifying the mediating role of socioeconomic status on the relationship between schizophrenia and major depressive disorder: a Mendelian randomisation analysis.

Depressive disorder prevalence in patients with schizophrenia has been reported to be 40%. People with low socioeconomic status (SES) are more likely to suffer from schizophrenia and major depressive disorder (MDD). However, the causal relationship between schizophrenia and depression and the potential mediating role of SES remains unclear. Two-sample Mendelian randomization (MR) analyses were conducted to explore the bidirectional causal relationship between schizophrenia and MDD with the largest sample size of European ancestry from public genome-wide association studies (sample size ranged from 130,644 to 480,359). Inverse variance weighted (IVW) method was used as the primary analysis, and several canonical MR methods were used as validation analyses. The mediating role of SES (educational years, household income, employment status, and Townsend deprivation index) was estimated by the two-step MR method. MR analyses showed that genetically predicted schizophrenia was associated with an increased risk of MDD (IVW odds ratio [OR] = 1.137 [95% CI 1.095, 1.181]). Reversely, MDD was also associated with an increased risk of schizophrenia (IVW OR = 1.323 [95% CI 1.118, 1.565]). The mediation analysis via the two-step MR method revealed that the causal effect of schizophrenia on MDD was partly mediated by the Townsend deprivation index with a proportion of 10.27%, but no significant mediation effect was found of SES on the causal effect of MDD on schizophrenia. These results suggest a robust bidirectional causal effect between schizophrenia and MDD. Patients with schizophrenia could benefit from the early and effective intervention of the Townsend deprivation index.

Individual-level brain morphological similarity networks: Current methodologies and applications.

AIMS: The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks. BACKGROUND: There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders. CONCLUSION: This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.

Spontaneous brain activity abnormalities in migraine: A meta-analysis of functional neuroimaging.

Neuroimaging studies have demonstrated that migraine is accompanied by spontaneous brain activity alterations in specific regions. However, these findings are inconsistent, thus hindering our understanding of the potential neuropathology. Hence, we performed a quantitative whole-brain meta-analysis of relevant resting-state functional imaging studies to identify brain regions consistently involved in migraine. A systematic search of studies that investigated the differences in spontaneous brain activity patterns between migraineurs and healthy controls up to April 2022 was conducted. We then performed a whole-brain voxel-wise meta-analysis using the anisotropic effect size version of seed-based d mapping software. Complementary analyses including jackknife sensitivity analysis, heterogeneity test, publication bias test, subgroup analysis, and meta-regression analysis were conducted as well. In total, 24 studies that reported 31 datasets were finally eligible for our meta-analysis, including 748 patients and 690 controls. In contrast to healthy controls, migraineurs demonstrated consistent and robust decreased spontaneous brain activity in the angular gyrus, visual cortex, and cerebellum, while increased activity in the caudate, thalamus, pons, and prefrontal cortex. Results were robust and highly replicable in the following jackknife sensitivity analysis and subgroup analysis. Meta-regression analyses revealed that a higher visual analog scale score in the patient sample was associated with increased spontaneous brain activity in the left thalamus. These findings provided not only a comprehensive overview of spontaneous brain activity patterns impairments, but also useful insights into the pathophysiology of dysfunction in migraine.

Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis.

Neuroimaging studies have shown that schizophrenia is associated with disruption of resting-state local functional connectivity. However, these findings vary considerably, which hampers our understanding of the underlying pathophysiological mechanisms of schizophrenia. Here, we performed an updated and extended meta-analysis to identify the most consistent changes of local functional connectivity measured by regional homogeneity (ReHo) in schizophrenia. Specifically, a systematic search of ReHo studies in patients with schizophrenia in PubMed, Embase, and Web of Science identified 18 studies (20 datasets), including 652 patients and 596 healthy controls. In addition, we included three whole-brain statistical maps of ReHo differences calculated based on independent datasets (163 patients and 194 controls). A voxel-wise meta-analysis was then conducted to investigate ReHo alterations and their relationship with clinical characteristics using the newly developed seed-based d mapping with permutation of subject images (SDM-PSI) meta-analytic approach. Compared with healthy controls, patients with schizophrenia showed significantly higher ReHo in the bilateral medial superior frontal gyrus, while lower ReHo in the bilateral postcentral gyrus, right precentral gyrus, and right middle occipital gyrus. The following sensitivity analyses including jackknife analysis, subgroup analysis, heterogeneity test, and publication bias test demonstrated that our results were robust and highly reliable. Meta-regression analysis revealed that illness duration was negatively correlated with ReHo abnormalities in the right precentral/postcentral gyrus. This comprehensive meta-analysis not only identified consistent and reliably aberrant local functional connectivity in schizophrenia but also helped to further deepen our understanding of its pathophysiology.

Total Brain Volumetric Measures and Schizophrenia Risk: A Two-Sample Mendelian Randomization Study.

Schizophrenia (SCZ) is an idiopathic psychiatric disorder with a heritable component and a substantial public health impact. Although abnormalities in total brain volumetric measures (TBVMs) have been found in patients with SCZ, it is still unknown whether these abnormalities have a causal effect on the risk of SCZ. Here, we performed a Mendelian randomization (MR) study to investigate the possible causal associations between each TBVM and SCZ risk. Specifically, genome-wide association study (GWAS) summary statistics of total gray matter volume, total white matter volume, total cerebrospinal fluid volume, and total brain volume were obtained from the United Kingdom Biobank database (33,224 individuals), and SCZ GWAS summary statistics were provided by the Psychiatric Genomics Consortium (150,064 individuals). The main MR analysis was conducted using the inverse variance weighted method, and other MR methods, including MR-Egger, weighted median, simple mode, and weighted mode methods, were performed to assess the robustness of our findings. For pleiotropy analysis, we employed three approaches: MR-Egger intercept, MR-PRESSO, and heterogeneity tests. No TBVM was causally associated with SCZ risk according to the MR results, and no significant pleiotropy or heterogeneity was found for instrumental variables. Taken together, this study suggested that alterations in TBVMs were not causally associated with the risk of SCZ.