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    Area of Science:

    • Computational psychiatry
    • Machine learning for mental health
    • Multimodal data fusion

    Background:

    • Major depressive disorder (MDD) is a prevalent and severe mental illness with significant societal impact.
    • Existing multimodal methods for MDD detection show promise but overlook inter-modal heterogeneity/homogeneity and feature separability.
    • There is a need for advanced fusion strategies that capture complex relationships within and between different data modalities and subjects.

    Purpose of the Study:

    • To propose a novel graph neural network (GNN)-based multimodal fusion strategy for enhanced major depressive disorder (MDD) detection.
    • To address limitations of previous methods by investigating heterogeneity/homogeneity among psychophysiological modalities and inter-subject relationships.
    • To develop a robust model that ensures data fidelity and achieves a compact multimodal representation for accurate MDD classification.

    Main Methods:

    • Developed a modal-shared and modal-specific graph neural network (GNN) architecture to extract inter- and intra-modal characteristics.
    • Incorporated a reconstruction network to maintain fidelity within individual data modalities.
    • Utilized an attention mechanism to generate a compact multimodal representation for MDD detection.

    Main Results:

    • The proposed GNN-based multimodal fusion strategy demonstrated superior performance in MDD detection.
    • The method effectively captured heterogeneity and homogeneity across different psychophysiological modalities.
    • Experiments on two public depression datasets confirmed the algorithm's effectiveness and robustness.

    Conclusions:

    • The modal-shared, modal-specific GNN approach offers a significant advancement in multimodal fusion for major depressive disorder detection.
    • This strategy successfully addresses the limitations of earlier methods by considering complex data relationships and ensuring feature compactness.
    • The findings highlight the potential of advanced GNN architectures in improving the accuracy and reliability of computational psychiatry tools.