  Introduction: Identifying neurobiological differences between patients
suffering from Major Depressive Disorder (MDD) and healthy individuals has been
a mainstay of clinical neuroscience for decades. However, recent meta- and
mega-analyses have raised concerns regarding the replicability and clinical
relevance of brain alterations in depression. Methods: Here, we systematically
investigate healthy controls and MDD patients across a comprehensive range of
modalities including structural magnetic resonance imaging (MRI), diffusion
tensor imaging, functional task-based and resting-state MRI under near-ideal
conditions. To this end, we quantify the upper bounds of univariate effect
sizes, predictive utility, and distributional dissimilarity in a fully
harmonized cohort of N=1,809 participants. We compare the results to an MDD
polygenic risk score (PRS) and environmental variables. Results: The upper
bound of the effect sizes range from partial eta squared = .004 to .017,
distributions overlap between 89% and 95%, with classification accuracies
ranging between 54% and 55% across neuroimaging modalities. This pattern
remains virtually unchanged when considering only acutely or chronically
depressed patients. Differences are comparable to those found for PRS, but
substantially smaller than for environmental variables. Discussion: We provide
a large-scale, multimodal analysis of univariate biological differences between
MDD patients and controls and show that even under near-ideal conditions and
for maximum biological differences, deviations are extremely small and
similarity dominates. We sketch an agenda for a new focus of future research in
biological psychiatry facilitating quantitative, theory-driven research, an
emphasis on computational psychiatry and multivariate machine learning
approaches, as well as the utilization of ecologically valid phenotyping.
