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

Area of Science:

• * Medical Informatics
• * Machine Learning
• * Computational Neuroscience

Background:

• * Graph Neural Networks (GNNs) are a novel deep learning paradigm adept at modeling complex relational data in non-Euclidean domains.
• * Traditional deep learning (DL) methods face limitations with intricate medical datasets.
• * GNNs present unique advantages for healthcare applications, including functional connectivity analysis, electrical diagnostics, and anatomical modeling.

Purpose of the Study:

• * To provide a comprehensive review of current GNN architectures and their applications in healthcare.
• * To analyze the strengths and limitations of spectral and spatial GNN variants (e.g., GCNs, GATs).
• * To propose key research directions for advancing GNN technology in medical research.

Main Methods:

• * Review and analysis of existing GNN architectures and their healthcare applications.
• * Critical assessment of spectral and spatial GNN variants and spatio-temporal extensions.
• * Introduction of a conceptual framework for a new Temporal Multi-modal Attention Graph Neural Network (TMA-GNN).

Main Results:

• * Identified strengths and limitations of current GNN approaches in medical contexts.
• * Proposed five key research directions: dynamic graphs, multi-modal fusion, uncertainty-aware GNNs, explainable GNNs, and federated GNNs.
• * Introduced the TMA-GNN architecture for longitudinal patient modeling and clinical trial optimization.

Conclusions:

• * GNNs are poised to play a pivotal role in precision medicine, disease progression modeling, and treatment personalization.
• * Future research should focus on dynamic, multi-modal, uncertainty-aware, explainable, and federated GNN frameworks.
• * The conceptual TMA-GNN framework offers a promising direction for neurological disorder modeling and clinical trial optimization.