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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
651

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  3. Whole-brain Task Fmri Decoding Using Stage-wise Residual-optimized 3d Convnext With Layer-global Response Normalization.
  1. Home
  3. Whole-brain Task Fmri Decoding Using Stage-wise Residual-optimized 3d Convnext With Layer-global Response Normalization.

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Whole-Brain Task fMRI Decoding Using Stage-Wise Residual-Optimized 3D ConvNeXt With Layer-Global Response

Ji-Hye Lim, Hyun-Chul Kim

    IEEE Journal of Biomedical and Health Informatics
    |November 10, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    A new 3D ConvNeXt framework enhances brain state decoding from functional magnetic resonance imaging (fMRI) data. This advanced deep learning model improves accuracy and interpretability for cognitive neuroscience and clinical applications.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Decoding brain states from functional magnetic resonance imaging (fMRI) is crucial for cognitive neuroscience and clinical applications.
    • Current deep learning models face challenges in balancing task generalization, spatial detail, and interpretability in fMRI analysis.

    Purpose of the Study:

    • To introduce a novel 3D ConvNeXt framework for whole-brain task-fMRI decoding.
    • To improve the accuracy, efficiency, and interpretability of fMRI decoding models.

    Main Methods:

    • Developed a 3D ConvNeXt framework incorporating layer-global response normalization (LN-GRN) and stage-wise residual connections.
    • Evaluated the model on the Human Connectome Project dataset across seven cognitive domains.
    • Utilized feature diversity analyses and uniform manifold approximation and projection (UMAP) for clustering and saliency mapping.

    Main Results:

    • The proposed framework consistently outperformed conventional and specialized 3D fMRI architectures.
    • LN-GRN improved feature separability, and restricted residual connections enhanced efficiency without sacrificing accuracy.
    • Saliency mapping revealed neuroanatomically meaningful activation patterns, confirming model interpretability.

    Conclusions:

    • The 3D ConvNeXt framework offers a robust, efficient, and interpretable solution for fMRI decoding, even with limited data.
    • The model provides neuroscientific insights by linking predictions to functional brain anatomy.
    • This approach shows significant promise for advancing cognitive neuroscience and clinical neuroimaging.