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High-dimensional Many-to-many-to-many Mediation Analysis.

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    This study introduces Many-to-Many-to-Many (MMM) mediation analysis for complex, high-dimensional data. The framework identifies genetic-neural-cognitive pathways and enhances prediction accuracy in scientific research.

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    Area of Science:

    • Biostatistics
    • Genetics
    • Neuroscience

    Background:

    • High-dimensional mediation analysis is crucial for understanding complex biological systems.
    • Existing methods struggle with multivariate exposures, mediators, and outcomes simultaneously.
    • The need for a framework handling many-to-many-to-many relationships is evident.

    Purpose of the Study:

    • To develop and validate a Many-to-Many-to-Many (MMM) mediation analysis framework.
    • To enable simultaneous variable selection, indirect effect estimation, and outcome prediction in high-dimensional settings.
    • To apply the MMM framework to genetic and neuroimaging data in Alzheimer's disease research.

    Main Methods:

    • Formalized the problem as Many-to-Many-to-Many (MMM) mediation analysis.
    • Developed methods for simultaneous variable selection and indirect effect matrix estimation.
    • Validated the framework through simulations and application to Alzheimer's Disease Neuroimaging Initiative data.

    Main Results:

    • The MMM mediation analysis framework demonstrates consistency and asymptotic normality.
    • Simulation studies confirmed its finite-sample performance, convergence, and robustness.
    • Identified significant many-to-many-to-many genetic-neural-cognitive pathways in Alzheimer's disease.
    • Improved out-of-sample classification and prediction performance for cognitive and diagnostic outcomes.

    Conclusions:

    • The MMM mediation analysis provides a powerful tool for investigating complex, high-dimensional multi-layer pathways.
    • The framework offers biologically interpretable insights into gene-brain-cognition relationships.
    • Statistical methodology advancements are vital for complex scientific investigations.