Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities

Abstract

Importance: Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression.

Objective: To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables.

Design, setting, and participants: This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022.

Main outcomes and measures: Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status.

Results: A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables.

Conclusions and relevance: Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

Figure 1.. Research Design and Analytical Procedure

Schematic representation of the research design and analytical procedure. For all modalities, standard univariate models are calculated to find the single variable showing the largest difference between healthy individuals and those with depression, representing an upper bound for univariate group differences. Effect size (η²), distributional overlap, and predictive utility are estimated for these peak variables of the different modalities. fMRI, functional magnetic resonance imaging; MACS, Marburg-Münster Affective Cohort Study.

Figure 2.. Effect Size, Classification Accuracy, and Distributional Overlap

Partial effect size (η²) of single variables displaying the overall largest effect in each modality. Error bars indicate upper and lower bounds for bootstrapped CIs for partial η². Balanced classification accuracy for all modalities based on logistic regression of single variables displaying the largest effect. Kernel density estimation plots of deconfounded values, including distributional overlap for healthy participants and those with depression are plotted on the right side of the figure. DTI indicates diffusion-tensor imaging; FA, fractional anisotropy; fALFF, fractional amplitude of low-frequency fluctuation; fMRI, functional magnetic resonance imaging; MDD, major depressive disorder; PRS, polygenic risk score; VBM, voxel-based morphometry.

Figure 3.. Effect Size, Predictive Utility, and Distributional Overlap of the Best Modality

Distributional overlap, effect size (η²), and classification performance for the single variable displaying the largest effect across all neuroimaging modalities (resting-state functional magnetic resonance imaging [fMRI] connectivity between a region of the right peripheral visual network and a region of the somatomotor network). A, Histogram with Gaussian kernel density estimation as a solid line and box plot of the confound-corrected values of the resting-state region of interest (ROI) to ROI connectivity displaying the largest effect. Middle line of the box plot represents the median value; the end of the boxes represents the lower and upper quartile. The boundaries of the whiskers are found within 1.5 IQR. B, Partial η² analysis of variance (ANOVA) effect size for the strongest resting-state connectivity. Light blue indicates the upper bound of a bootstrapped 95% CI. C, Receiver operating characteristic (ROC) curve for logistic regression classification based on the single variable displaying the largest effect. AUROC indicates area under the ROC curve; HC, healthy control; MDD, major depressive disorder.