Frequency Feature Fusion Graph Network For Depression Diagnosis Via fNIRS

• URL: http://arxiv.org/abs/2504.21064v1
• Date: Tue, 29 Apr 2025 11:10:05 GMT
• Title: Frequency Feature Fusion Graph Network For Depression Diagnosis Via fNIRS
• Authors: Chengkai Yang, Xingping Dong, Xiaofen Zong,
• Abstract summary: We introduce a novel and effective biomarker for depression diagnosis by leveraging the discrete Fourier transform (DFT)<n>Our model was trained on a dataset comprising 1,086 subjects, which is over 10 times larger than previous datasets in the field of depression diagnosis.
• Score: 10.871208380856611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven approaches for depression diagnosis have emerged as a significant research focus in neuromedicine, driven by the development of relevant datasets. Recently, graph neural network (GNN)-based models have gained widespread adoption due to their ability to capture brain channel functional connectivity from both spatial and temporal perspectives. However, their effectiveness is hindered by the absence of a robust temporal biomarker. In this paper, we introduce a novel and effective biomarker for depression diagnosis by leveraging the discrete Fourier transform (DFT) and propose a customized graph network architecture based on Temporal Graph Convolutional Network (TGCN). Our model was trained on a dataset comprising 1,086 subjects, which is over 10 times larger than previous datasets in the field of depression diagnosis. Furthermore, to align with medical requirements, we performed propensity score matching (PSM) to create a refined subset, referred to as the PSM dataset. Experimental results demonstrate that incorporating our newly designed biomarker enhances the representation of temporal characteristics in brain channels, leading to improved F1 scores in both the real-world dataset and the PSM dataset. This advancement has the potential to contribute to the development of more effective depression diagnostic tools. In addition, we used SHapley Additive exPlaination (SHAP) to validate the interpretability of our model, ensuring its practical applicability in medical settings.

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