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Adaptive High-Order Fusion Learning for Brain Disorder Detection.

Hengsheng Tang, Junji Jiang, Junhao Zhang

    IEEE Journal of Biomedical and Health Informatics
    |December 24, 2025
    PubMed
    Summary

    This study introduces an adaptive framework to fuse functional brain networks (FBNs) of varying orders, optimizing brain disorder detection. It found third-order FBNs are key for autism spectrum disorder and second-order FBNs for major depressive disorder.

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    Area of Science:

    • Neuroscience
    • Computational Psychiatry
    • Machine Learning

    Background:

    • Functional brain networks (FBNs) are crucial for understanding neurological and mental disorders.
    • Estimating FBNs from neuroimaging data is essential for diagnostic performance.
    • The optimal order of FBNs for discriminative power remains debated.

    Purpose of the Study:

    • To develop an adaptive high-order FBN fusion learning framework (AHFL) for enhanced brain disorder detection.
    • To identify the optimal order of FBNs for specific neurological and mental disorders.
    • To leverage attention mechanisms for improved FBN analysis.

    Main Methods:

    • Constructed a series of FBNs with continuously increasing orders.
    • Proposed a data-driven approach to evaluate the contribution of each FBN order.

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  • Employed a self-attention mechanism for contextual dependency capture and fusion of multi-order FBNs.

    • Main Results:

    • The AHFL framework demonstrated superior performance over baseline methods.
    • Third-order FBNs showed the highest weights for autism spectrum disorder (ASD) detection.
    • Second-order FBNs were most effective for major depressive disorder (MDD) identification.

    Conclusions:

    • The study advances the identification of discriminative high-order FBNs.
    • Established a generalizable diagnostic framework for brain disorders using adaptive FBN fusion.
    • Highlighted the differential importance of FBN orders for specific disorders like ASD and MDD.