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Alzheimer's Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features.

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    This study introduces a new framework combining brain network node and edge features for improved Alzheimer's disease (AD) and mild cognitive impairment (MCI) classification using MRI scans. The method shows promise for clinical diagnosis.

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    Area of Science:

    • Neuroimaging
    • Machine Learning
    • Medical Diagnostics

    Background:

    • Brain network abnormalities are key indicators of Alzheimer's disease (AD) and mild cognitive impairment (MCI).
    • Previous methods utilized only edge features of brain networks for AD classification.
    • Node features of brain networks have not been integrated with edge features for enhanced classification.

    Purpose of the Study:

    • To propose a novel framework combining multiple kernels to integrate both node and edge features of brain networks for AD classification.
    • To evaluate the proposed framework's efficacy in classifying AD, MCI (including MCIc and MCInc), and healthy controls (HC).
    • To assess the potential of 3D texture features in MRI for detecting subtle tissue differences and correlating with cognitive impairment severity.

    Main Methods:

    • A multi-kernel learning framework was developed to combine brain network edge and node features.
    • The approach was evaluated using 710 subjects (230 HC, 280 MCI, 200 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
    • Ten-fold cross-validation was employed to assess classification performance.

    Main Results:

    • The proposed method demonstrated superior performance compared to existing AD classification techniques.
    • The framework proved efficient and promising for clinical applications in diagnosing AD using MRI.
    • 3D texture analysis effectively detected subtle tissue differences among AD, MCI, and HC groups.
    • Texture features from MRI images showed a potential correlation with the severity of AD-related cognitive impairment.

    Conclusions:

    • The combined use of node and edge features in brain networks significantly enhances AD and MCI classification accuracy.
    • The proposed multi-kernel framework offers a promising and efficient tool for clinical AD diagnosis via MRI.
    • 3D texture features derived from MRI are valuable for identifying AD/MCI and assessing cognitive decline severity.