Classification of Major Depressive Disorder Using Graph Attention Mechanism with Multi-Site rs-fMRI Data.

Abstract

Major Depressive Disorder (MDD) significantly impacts global health, impairing individual functioning and increasing socioeconomic burden. Developing innovative, interpretable approaches for its identification is essential for improving diagnosis and guiding treatment. This study introduces a novel framework designed to classify MDD using resting-state functional MRI (rs-fMRI) data. Our framework follows three stages: First, Node2Vec extracts rich, low-dimensional brain region embeddings from functional connectivity (FC) networks, capturing their complex topological information. Second, these informative embeddings feed a Graph Attention Network (GAT) which, via multi-head attention, identifies and weighs discriminative inter-regional functional connections, refining them into a potent graph representation. Third, these GAT-derived representations are classified by an ensemble classifier (Random Forest, SVM, MLP) for robust MDD identification. The model achieved classification accuracies of 78.73% and 92.94% on the REST-meta-MDD and SRPBS-MDD datasets, respectively. Moreover, the attention mechanism revealed that resting-state functional connectivity of regions within the Default Mode Network (DMN) and Frontoparietal Network (FPN) were among the most discriminative features for distinguishing MDD from healthy controls. The attention mechanism enhanced interpretability by highlighting significant brain regions linked to MDD. Compared to traditional methods, this GNN-based approach effectively captures complex brain connectivity patterns and offers improved interpretability, ultimately aiding healthcare professionals in diagnosing MDD more accurately.