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<p>Graph Neural Networks (GNNs) have emerged as a novel paradigm that enables scientists to model complex relational data in medical applications, offering unique advantages over traditional deep learning (DL) approaches for non-Euclidean domains. This paper provides a comprehensive review of current GNN architectures and their healthcare applications, with a focus on functional connectivity analysis, electrical-based diagnostics, and anatomical structure modeling. We analyze the strengths and limitations of spectral and spatial GNN variants, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and spatio-temporal extensions. Based on our critical assessment of the state-of-the-art innovations, we propose several key directions for medical researchers actively developing GNN technology: (1) Dynamic graph representation learning to capture evolving physiological processes; (2) Multi-modal fusion techniques to integrate heterogeneous biomedical data streams; (3) Uncertainty-aware GNNs for robust clinical decision support; (4) Explainable GNN architectures to enhance interpretability for healthcare practitioners; and (5) Federated GNN frameworks to enable privacy-preserving collaborative learning across institutions. We also introduce a new Temporal Multi-modal Attention Graph Neural Network (TMA-GNN) architecture designed explicitly for longitudinal patient modeling and clinical trial optimization. Our TMA-GNN incorporates multi-head attention mechanisms, temporal edge construction, and a custom loss function to encourage temporal consistency in predictions. We introduce a conceptual framework for the Temporal Multi-modal Attention Graph Neural Network (TMA-GNN), which is designed to support disease progression modeling and clinical trial optimization in neurological disorders. Although the proposed model architecture is technically detailed, this manuscript focuses on the conceptual and methodological design, rather than presenting experimental results. By addressing these proposed research directions, we envision GNNs will play an increasingly pivotal role in precision medicine, disease progression modeling, and treatment personalization.</p>

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Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Rahul Kumar,
  • Kyle Sporn,
  • Ethan Waisberg,
  • Joshua Ong,
  • Phani Paladugu,
  • Tejas Sekhar,
  • Tamer Hage,
  • Swapna Vaja,
  • Nicolas Nelson,
  • Mouayad Masalkhi,
  • Ryung Lee,
  • Dylan Amiri,
  • Chirag Gowda,
  • Alex Ngo,
  • Shreya Raj,
  • Ram Jagadeesan,
  • Nasif Zaman,
  • Alireza Tavakkoli,
  • Shashinath Chandrahasegowda,
  • Tarikere Kumar

摘要

Graph Neural Networks (GNNs) have emerged as a novel paradigm that enables scientists to model complex relational data in medical applications, offering unique advantages over traditional deep learning (DL) approaches for non-Euclidean domains. This paper provides a comprehensive review of current GNN architectures and their healthcare applications, with a focus on functional connectivity analysis, electrical-based diagnostics, and anatomical structure modeling. We analyze the strengths and limitations of spectral and spatial GNN variants, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and spatio-temporal extensions. Based on our critical assessment of the state-of-the-art innovations, we propose several key directions for medical researchers actively developing GNN technology: (1) Dynamic graph representation learning to capture evolving physiological processes; (2) Multi-modal fusion techniques to integrate heterogeneous biomedical data streams; (3) Uncertainty-aware GNNs for robust clinical decision support; (4) Explainable GNN architectures to enhance interpretability for healthcare practitioners; and (5) Federated GNN frameworks to enable privacy-preserving collaborative learning across institutions. We also introduce a new Temporal Multi-modal Attention Graph Neural Network (TMA-GNN) architecture designed explicitly for longitudinal patient modeling and clinical trial optimization. Our TMA-GNN incorporates multi-head attention mechanisms, temporal edge construction, and a custom loss function to encourage temporal consistency in predictions. We introduce a conceptual framework for the Temporal Multi-modal Attention Graph Neural Network (TMA-GNN), which is designed to support disease progression modeling and clinical trial optimization in neurological disorders. Although the proposed model architecture is technically detailed, this manuscript focuses on the conceptual and methodological design, rather than presenting experimental results. By addressing these proposed research directions, we envision GNNs will play an increasingly pivotal role in precision medicine, disease progression modeling, and treatment personalization.