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A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or

Dongren Yao, Jing Sui, Mingliang Wang

    IEEE Transactions on Medical Imaging
    |January 14, 2021
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    This study introduces a novel multi-scale graph convolutional network for analyzing brain connectivity in mental disorders. The method enhances diagnostic accuracy by considering multi-scale networks and subject relationships.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Machine Learning

    Background:

    • Brain connectivity alterations are linked to mental disorders, detectable via fMRI and dMRI.
    • Extracting meaningful insights from complex brain network data remains challenging.
    • Current graph convolutional network (GCN) methods often analyze networks at a single spatial scale and overlook high-order subject relationships.

    Purpose of the Study:

    • To develop a novel Mutual Multi-Scale Triplet Graph Convolutional Network (MMTGCN) for improved brain disorder diagnosis.
    • To overcome the limitations of single-scale analysis and incorporate high-order subject associations in brain connectivity analysis.
    • To enhance the identification of mental disorders using both functional and structural connectivity data.

    Main Methods:

    • Constructed multi-scale brain connectivity networks using varying region-of-interest (ROI) parcellation scales.
    • Developed a Triplet GCN (TGCN) module to learn network representations incorporating triplet relationships among subjects.
    • Implemented a template mutual learning strategy to collaboratively train multi-scale TGCNs for disease classification.

    Main Results:

    • The MMTGCN model demonstrated superior performance in identifying three types of brain disorders.
    • Experiments were conducted on 1,160 subjects using fMRI and dMRI data across three datasets.
    • The proposed method outperformed several existing state-of-the-art approaches in brain disorder diagnosis.

    Conclusions:

    • The MMTGCN effectively analyzes multi-scale functional and structural brain connectivity for enhanced disorder identification.
    • The integration of multi-scale analysis and triplet relationships significantly improves diagnostic accuracy.
    • This approach offers a promising advancement in the computational analysis of brain networks for psychiatric research.