Elliot S. Gershon

This study identifies three psychosis neurosubtypes using imaging data, showing distinct connectivity patterns and supporting their biological validity through relative alignment.

The paper analyzes psychosis biotypes using ancestry-adjusted polygenic risk scores and identifies genomic associations with specific biotypes in brain transcriptomes.

This study identifies population-specific eQTLs in brain tissues from European, African American, and Asian ancestry individuals, revealing new schizophrenia risk genes and variants missed in European-only datasets.

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

This study shows that bipolar disorder patient-derived neurons exhibit circadian rhythm abnormalities, with lithium non-responders showing more severe deficits that can be partially reversed by temperature entrainment.

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

The paper discusses challenges in genetic analysis of deep phenotyping projects in common disorders and highlights advances in imputation and GWAS methods to address these challenges.

The paper reports GWAS findings linking common variants in NRXN1, LRP1B, and RORA to increased ventricular volumes in psychosis, using deep phenotyping data from 1,115 cases and controls.

The paper investigates the association between schizophrenia genetic risk and cognitive performance in healthy individuals, finding a link with current cognitive abilities but not with premorbid intelligence or education.