Faculty, Staff and Student Publications
Language
English
Publication Date
3-1-2026
Journal
Molecular Psychiatry
DOI
10.1038/s41380-025-03273-w
PMID
41044403
PMCID
PMC12916314
PubMedCentral® Posted Date
10-3-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7012 participants from 31 sites (N = 2772 MDD and N = 4240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from other MRI modalities such as fMRI and DWI will lead to a higher performance in this diagnostic task.
Keywords
Humans, Major Depressive Disorder, Male, Female, Magnetic Resonance Imaging, Adult, Support Vector Machine, Middle Aged, Deep Learning, Brain, Neuroimaging, Machine Learning, Image Processing, Computer-Assisted, Depression, Neuroscience
Published Open-Access
yes
Recommended Citation
Goya-Maldonado, Roberto; Erwin-Grabner, Tracy; Zeng, Ling-Li; et al., "Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness With a Deep and a Shallow Learning Model" (2026). Faculty, Staff and Student Publications. 6548.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6548
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