Myrna M. Weissman

This paper uses deep learning on pre-treatment reward processing fMRI and clinical data to predict antidepressant outcomes, achieving up to 37% variance explained and remission prediction with NNT 2-4.

This paper develops a transcriptome-based polygenic risk score (T-PRS) linking depression-related cortical gene expression changes to brain morphology and symptom severity in MDD.

This study assesses intra-session test-retest reliability of functional connectivity in infants, finding poor edge-level reliability but moderate-to-good subject-level reliability for major networks.

This paper shows that GWAS using minimal phenotyping for depression identifies non-specific genetic factors shared with other psychiatric conditions, not specific to major depressive disorder.

This study investigates whether genetic variation in the Major Histocompatibility Complex (MHC) is associated with depression risk, finding no significant associations at a conservative threshold.

This paper conducts a GWAS on suicide attempt across psychiatric disorders, identifying genetic loci and showing polygenic risk for major depression predicts suicide attempt in multiple disorders.

ComBat, an empirical Bayes location-and-scale model from genomics, removes additive and multiplicative scanner effects from regional cortical thickness across 11 scanners while preserving age associations.