Graph Neural Networks with Transformer Fusion of Brain Connectivity Dynamics and Tabular Data for Forecasting Future Tobacco Use

• URL: http://arxiv.org/abs/2512.23137v1
• Date: Mon, 29 Dec 2025 01:58:20 GMT
• Title: Graph Neural Networks with Transformer Fusion of Brain Connectivity Dynamics and Tabular Data for Forecasting Future Tobacco Use
• Authors: Runzhi Zhou, Xi Luo,
• Abstract summary: We introduce a time-aware graph neural network model with transformer fusion (GNN-TF)<n>By incorporating non-Euclidean and Euclidean sources of information from a longitudinal resting-state fMRI dataset, the GNN-TF enables a comprehensive analysis.<n>It delivers superior predictive accuracy for predicting future tobacco usage.
• Score: 7.476018348880164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating non-Euclidean brain imaging data with Euclidean tabular data, such as clinical and demographic information, poses a substantial challenge for medical imaging analysis, particularly in forecasting future outcomes. While machine learning and deep learning techniques have been applied successfully to cross-sectional classification and prediction tasks, effectively forecasting outcomes in longitudinal imaging studies remains challenging. To address this challenge, we introduce a time-aware graph neural network model with transformer fusion (GNN-TF). This model flexibly integrates both tabular data and dynamic brain connectivity data, leveraging the temporal order of these variables within a coherent framework. By incorporating non-Euclidean and Euclidean sources of information from a longitudinal resting-state fMRI dataset from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), the GNN-TF enables a comprehensive analysis that captures critical aspects of longitudinal imaging data. Comparative analyses against a variety of established machine learning and deep learning models demonstrate that GNN-TF outperforms these state-of-the-art methods, delivering superior predictive accuracy for predicting future tobacco usage. The end-to-end, time-aware transformer fusion structure of the proposed GNN-TF model successfully integrates multiple data modalities and leverages temporal dynamics, making it a valuable analytic tool for functional brain imaging studies focused on clinical outcome prediction.

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