Corina Nagy

The paper identifies spatially localized astrocytic dysfunction in depression using multi-omics data and shows its role in disease pathology.

The paper identifies sex-specific regulatory dynamics in adolescent brain development that may contribute to depression vulnerability through hormone-sensitive gene expression and chromatin accessibility patterns.

This study characterizes the human uncinate fasciculus using multimodal approaches, finding no group differences in histology but identifying differentially expressed genes and age-related changes.

The paper identifies a glucocorticoid-responsive gene network in the anterior cingulate cortex that moderates the link between early-life adversity and depression risk in adult females.

This study identifies differential chromatin architecture and risk variants in specific brain cell types associated with major depressive disorder.

The paper maps whole-brain cell-cell interactions across neurodegenerative diseases and identifies communication axes linked to regional brain vulnerability.

The paper investigates the functional relationship between a suicide attempt-associated SNP on chromosome 7 and gene expression in brain regions, identifying FOXP2 as a key gene involved.

The paper investigates how childhood abuse affects the locus coeruleus-noradrenergic system in post-mortem brain samples, finding reduced neuron density and differential gene expression in resilient individuals.

ECLARE integrates unpaired single-cell multi-omic data using multi-teacher contrastive learning, improving cell pairing accuracy and biological structure preservation in depression studies.

The paper uses spatial transcriptomics and FISH to analyze adult hippocampal neurogenesis in the human dentate gyrus across ages, finding few neural stem cell markers but significant DCX expression with mixed inhibitory and excitatory phenotypes.

This study identifies proteomic changes in the human male olfactory bulb associated with depression, highlighting astrocytic dysfunction and species-specific differences in mouse models.

This study develops a machine learning tool to integrate multi-omic data and identify biomarkers for antidepressant response, finding dynamic immune and inflammatory changes are associated with treatment success.

This study identifies expression networks of MDD risk genes linked to astrocyte fatty acid metabolism and suicidal ideation, suggesting PPARA as a therapeutic target.

The paper examines changes in substance P, NPY, and their receptors in major depression by analyzing transcript levels in brain regions of suicide victims and controls.

This study shows that child abuse is linked to increased perineuronal net recruitment in the ventromedial prefrontal cortex, potentially through oligodendrocyte progenitor cell activity.

This study examines neuron-derived extracellular vesicles from plasma to identify size and miRNA cargo changes linked to antidepressant response in depressed patients.

The paper investigates how psychiatric disease and aging affect FKBP5 expression in the human brain, identifying supragranular neurons as key targets.

This paper introduces scGAN, a method to reduce batch effects in scRNA-seq data by using a generative adversarial network, and shows it improves clustering and gene identification in psychiatric disorders.

The paper uses single-nucleus RNA sequencing to identify gene expression changes in specific cell types associated with major depressive disorder.