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Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, an architecture that integrates Kolmogorov-Arnold Networks (KANs) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data from 91 subjects, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 5.2% in classification accuracy (62.6% vs. 57.4%) while providing interpretable insights into key brain regions associated with AD. This approach offers a robust and explainable tool for AD diagnosis, potentially facilitating earlier intervention and more personalized treatment planning.

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Explainable Diagnosis of Alzheimer’s Disease Using Graph Kolmogorov-Arnold Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Tianqi Ding,
  • Dawei Xiang,
  • Keith E. Schubert,
  • Liang Dong

摘要

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, an architecture that integrates Kolmogorov-Arnold Networks (KANs) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data from 91 subjects, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 5.2% in classification accuracy (62.6% vs. 57.4%) while providing interpretable insights into key brain regions associated with AD. This approach offers a robust and explainable tool for AD diagnosis, potentially facilitating earlier intervention and more personalized treatment planning.