# Functional connectivity–based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach

## TL;DR

This study uses brain connectivity patterns from MRI scans to detect depression and identify three distinct subtypes, offering a more objective and biologically grounded approach to diagnosis and treatment.

## Contribution

The novel contribution is an ensemble graph neural network framework for classifying and subtyping MDD using functional connectivity data with cross-national validation.

## Key findings

- The classifier achieved 0.73 leave-one-site-out accuracy on the REST-meta-MDD cohort.
- Three MDD subtypes were identified with distinct connectivity signatures involving key brain networks.
- The method retained 0.78 sensitivity when transferred from the Chinese to the Japanese cohort.

## Abstract

Major depressive disorder (MDD) remains clinically diagnosed based on subjective symptoms rather than objective neurobiological markers, which limits diagnostic accuracy and the ability to tailor treatment. We present an ensemble hybrid framework that integrates graph neural networks (GNN) with unsupervised clustering to classify and subtype MDD using resting-state functional connectivity (rs-fMRI) profiles. A GNN was trained to distinguish MDD from healthy controls using functional connectivity derived brain graphs, and the resulting subject level embeddings were clustered to uncover subtype structure. We evaluated the approach on two public multisite cohorts, REST-meta-MDD (China; N = 1,604; 17 sites) and SRPBS (Japan; N = 446; 4 sites), using leave-one-site-out cross-validation and cross-national transfer. The classifier achieved 0.73 leave-one-site-out accuracy on REST-meta-MDD and retained 0.78 sensitivity when transferred from the Chinese to the Japanese cohort, outperforming BrainIB and CI GNN under the same protocol. To mitigate site related confounds, we applied a standardized preprocessing pipeline and ComBat harmonization. Clustering consistently identified three MDD subtypes with distinct connectivity signatures involving the default mode network and cerebellum, the insula-cingulum temporal circuit, and frontostriatal circuitry. These findings provide a reproducible and biologically interpretable stratification of MDD. Prospective studies will be needed to link these subtypes to treatment response and other clinically meaningful outcomes.

Depression affects hundreds of millions of people worldwide and is a leading cause of disability, yet diagnosis still relies on reported symptoms rather than brain-based measures. This symptom-based approach can lead to misdiagnosis, delayed treatment, and suboptimal outcomes. We developed a machine learning framework that uses brain connectivity patterns from MRI scans to detect depression and identify biologically distinct subtypes. Using data from more than 2,000 participants across China and Japan, our method not only accurately distinguished patients from healthy individuals but also revealed three reproducible subtypes linked to specific brain circuits. By enabling a more objective and biologically grounded classification, this work could improve treatment matching, reduce unnecessary interventions, and support more efficient use of healthcare resources. Ultimately, such approaches could help deliver personalized and biologically informed mental health care worldwide.