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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis.

Eunsong Kang, Da-Woon Heo, Jiwon Lee

    IEEE Transactions on Medical Imaging
    |November 29, 2023
    PubMed
    Summary

    This study introduces a unified deep learning framework for diagnosing neurological disorders using functional connectivity (FC). The novel approach integrates diagnosis and explanation, outperforming existing methods in identifying disease-related biomarkers.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Neurological disorder diagnosis often relies on analyzing functional connectivity (FC) using deep learning.
    • Current methods separate feature selection, extraction, and analysis, risking unreliable results and misdiagnosis.
    • Explainable models are used for biomarker discovery, but integration with diagnostic models is limited.

    Purpose of the Study:

    • To develop a unified deep learning framework integrating diagnosis and explanation for neurological disorders.
    • To improve the reliability and accuracy of disease identification and biomarker discovery from fMRI data.
    • To introduce a novel neuroscientific interpretation method via counter-condition analysis.

    Main Methods:

    • Proposed a unified framework integrating feature selection, extraction, and explanation stages.
    • Devised an adaptive attention network for individual-specific disease-related connection identification.
    • Developed a functional network relational encoder for global topological property summarization.
    • Introduced counter-condition analysis by simulating reversed diagnostic information in FC.

    Main Results:

    • The unified framework demonstrated superior performance in disease identification compared to competing methods.
    • Validated effectiveness using large resting-state fMRI datasets (ABIDE and REST-meta-MDD).
    • Identified disease-related neurological patterns through counter-condition analysis.

    Conclusions:

    • The proposed unified framework enhances the accuracy and reliability of neurological disorder diagnosis.
    • The novel counter-condition analysis offers valuable neuroscientific insights into disease mechanisms.
    • This integrated approach advances the application of deep learning in clinical neuroscience.