CNS neuroscience & therapeutics · 2026
Universum Inspired Graph Contrast Learning for Major Depressive Disorder Identification: In Functional Brain Network View.
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Qin Hao, Hong Guanghui, Ma Di, Wang Zeyi, Zhao Leina, Li Weikai, Xu Xiaowen
Аннотация
BACKGROUND: Major depressive disorder (MDD) is a serious psychological disorder marked by persistent feelings of sadness and a notable decline in daily functioning. Functional Brain Networks (FBNs) have become crucial biomarkers for the diagnosis of MDD, and Graph Convolutional Networks (GCNs) have demonstrated great potential in extracting FBN features. However, existing GCN methods typically depend on large-scale labeled datasets and lack clinical interpretability in MDD identification, which limits their application.
METHODS: We introduce Universum Inspired Graph Contrast Learning (Uni-GCL), a novel graph-based self-supervised learning framework to relieve the demand for training sample size. Additionally, to enhance the clinical interpretability of GCNs, we optimized the Gradient-weighted Class Activation Mapping (Grad-CAM) method to enable it to effectively pinpoint the crucial nodes within the GCN.
RESULTS: The Uni-GCL method outperformed previous state-of-the-art approaches. It improved accuracy by 3% at Site 21 and 5% at Site 1. Additionally, Uni-GCL identified that brain regions FGoperc. R, CAU. L, FFG. R, AMYG. L, PUT. L, AMYG. R, PUT. R, THA. L, IFGtriang. R, FFG. L, INS. L, INS. R, HIP. L and HIP. R make significant contributions to the diagnosis of MDD.
CONCLUSIONS: Uni-GCL can effectively reduce the sample size for model training and improve the diagnostic performance of MDD. Meanwhile, the adapted Grad-CAM provides an effective tool to enhance the interpretability of GCN.
METHODS: We introduce Universum Inspired Graph Contrast Learning (Uni-GCL), a novel graph-based self-supervised learning framework to relieve the demand for training sample size. Additionally, to enhance the clinical interpretability of GCNs, we optimized the Gradient-weighted Class Activation Mapping (Grad-CAM) method to enable it to effectively pinpoint the crucial nodes within the GCN.
RESULTS: The Uni-GCL method outperformed previous state-of-the-art approaches. It improved accuracy by 3% at Site 21 and 5% at Site 1. Additionally, Uni-GCL identified that brain regions FGoperc. R, CAU. L, FFG. R, AMYG. L, PUT. L, AMYG. R, PUT. R, THA. L, IFGtriang. R, FFG. L, INS. L, INS. R, HIP. L and HIP. R make significant contributions to the diagnosis of MDD.
CONCLUSIONS: Uni-GCL can effectively reduce the sample size for model training and improve the diagnostic performance of MDD. Meanwhile, the adapted Grad-CAM provides an effective tool to enhance the interpretability of GCN.