Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder (MDD), but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of this work were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

Source: Biological Psychiatry - January 25, 2024 Category: Psychiatry Authors: Katharine Dunlop, Logan Grosenick, Jonathan Downar, Fidel Vila-Rodriguez, Faith M. Gunning, Zafiris J. Daskalakis, Daniel M. Blumberger, Conor Liston Tags: Archival Report Source Type: research