Analysis of gene co-expression connectivity dynamics implicates aberrant neuron-oligodendroglia interactions in schizophrenia

Cortical Hub Vulnerability and Molecular Signatures of Sulcal Widening in Schizophrenia.

BACKGROUND Schizophrenia is increasingly conceptualized as a disorder of large-scale brain network organization arising from atypical neurodevelopment. However, the relationship between early-emerging cortical folding patterns and the maturation of the structural connectome remains poorly understood. METHODS We introduced a sulcal morphology-centered framework that integrated normative modeling of sulcal width with diffusion-derived structural connectivity and cortical transcriptomics in a large multisite cohort (n=5,392; 377 schizophrenia). Deviations from normative folding patterns were mapped to the structural connectome and the Allen Human Brain Atlas. RESULTS Individuals with schizophrenia exhibited widespread sulcal widening (30/40 sulci), primarily in frontal, temporal, and occipital regions. Nodal vulnerability followed a clear topological principle: sulci with higher degree centrality (sulcal network hubs) showed disproportionately greater widening (pspin=0.02). Transcriptomic integration identified a gene expression profile explaining 56.5% of the spatial variance in sulcal abnormalities (p ​=0.049). This profile was significantly enriched for synaptic signaling and energy metabolism genes, showed adult-onset expression bias, and was associated with common cross-disorder genetic risk. Conversely, genes with the opposite spatial weight showed significant prenatal expression bias and enrichment for rare disruptive variants associated with autism spectrum disorder. CONCLUSIONS These findings demonstrate that aberrant cortical folding in schizophrenia is constrained by network topology and molecular architecture. By linking macroscopic folding to metabolic and synaptic pathways, this work establishes sulcal morphology as a mechanistically grounded biomarker that may help differentiate the neurodevelopmental trajectories of psychiatric disorders.

Linking Gene Sequences to Brain Connectivity Alterations in Schizophrenia

Schizophrenia is a complex neuropsychiatric disorder characterized by altered brain connectivity patterns and strong genetic underpinnings. This study develops an intelligent computational system that integrates genomic data with functional magnetic resonance imaging to establish meaningful correlations between genetic variations in key schizophrenia-associated genes (DISC1, LRRTM1, DRD2) and altered brain connectivity patterns. The proposed methodology uses gene expression analysis combined with graph-based brain network analysis from fMRI data. Feature extraction involves constructing connectivity matrices and computing topological network measures such as clustering coefficients and global efficiency, while machine learning algorithms are trained to classify schizophrenia patients and identify at-risk individuals using integrated genomic and neuroimaging features. This research expects to deliver a comprehensive gene-brain analysis system capable of predicting schizophrenia risk based on these integrated profiles. It aims to identify novel biomarkers linking specific genetic variants to connectivity alterations in brain regions such as the prefrontal cortex and hippocampus. Anticipated outcomes include the development of interpretable visualizations, like brain maps and connectivity networks, offering insights into how key genes impact neural function. Ultimately, this interdisciplinary approach is designed to advance precision psychiatry by supporting earlier diagnosis and enabling more personalized treatment strategies.

Coordinated dysregulation of modular gene activity in human neuropathologies

Understanding which genes are reproducibly dysregulated in which cell types is foundational knowledge for efforts to slow or reverse pathologies. For neuropathologies, such efforts rely primarily on differential expression analysis of single-nucleus RNA-seq (snRNA-seq) data. However, this strategy suffers from experimental and statistical challenges that limit marker gene reproducibility. We describe a novel strategy called Covariation Projection Analysis (CoPA) that combines the power of bulk sampling with the precision of single-cell methods. By ‘projecting’ bulk gene coexpression modules onto pseudobulked snRNA-seq cell types, CoPA reveals the cellular origins of highly reproducible genomic programs and their relative importance among cell types. By comparing CoPA projection patterns between normal and pathological human brain samples using differential CoPA (dCoPA), we identify gene coexpression modules that are uniformly and reproducibly dysregulated in specific neocortical cell types in Alzheimer’s disease or schizophrenia. We share our findings through a novel web application called CoPA Cabana (https://oldhamlab.shinyapps.io/copacabana/).

Developmental and cellular vulnerabilities underlie genetic architecture of schizophrenia.

Cross-Modal Brain Network Disruptions in Schizophrenia: Insights from Multiplex Modeling and Transcriptomic Associations.

Multiomic Single-Nucleus Profiling Reveals Cell-Type-Specific Epigenetic and Transcriptional Dysregulation in Major Depressive Disorder Brain.

OBJECTIVE Major Depressive Disorder (MDD) is a leading global cause of disability, marked by persistent mood disturbances, cognitive deficits, and changes in prefrontal cortex neural circuitry. In this study, we aimed to define cell-type-specific molecular and regulatory mechanisms underlying MDD by mapping gene-expression and chromatin-accessibility changes in the dorsolateral PFC (dlPFC). METHODS Postmortem dlPFC (BA9) tissue from 7 MDD and 8 well-matched controls was analyzed using 10x Genomics snRNA-seq and paired ATAC+RNA multiome sequencing. Sequencing data were processed with Cell Ranger pipelines, nuclei were filtered for quality and doublets/debris, and datasets were integrated and clustered using Seurat/Signac packages. Differential gene expression, chromatin accessibility, and transcription factor motif activity were tested between MDD and controls within each cell type, followed by peak-to-gene linkage and GO/KEGG and PsyGeNET enrichment to interpret dysregulated regulatory mechanisms. RESULTS A total of 20 distinct clusters encompassing major neuronal and non-neuronal populations were identified. Differential analyses uncovered extensive cell type-specific changes in chromatin accessibility and gene expression, particularly within excitatory layer 5/6 and inhibitory Pvalb neurons, as well as glial and vascular populations. Functional enrichment indicated dysregulation of synaptic organization, neurotransmission, myelination, stress-response, and immune-regulatory pathways across neuronal and non-neuronal cells. Notably, glucocorticoid-responsive transcription factors NR3C1/NR3C2 exhibited conserved regulatory networks implicating stress signaling in MDD pathophysiology. CONCLUSIONS Together, these findings provide a comprehensive single-nucleus atlas of gene regulation in the MDD PFC, highlighting coordinated dysfunction across neurons, glia, and vascular cells.