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January 1, 2026IEEE Transactions on Biomedical Engineering

BC-GTM: A Bidirectional Causal Graph Transformer Mapping for Modeling Brain Structural and Functional Connectivity

Zhiwei Song Jingming Li Zhengyuan Lyu Si Fu Xiaojuan Guo

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Key Points

The aim is to develop a framework for effectively mapping brain structural and functional connectivity using causal graphs.
Developed a bidirectional causal graph transformer framework.
Utilized graph neural networks for enhanced interpretation.
Focused on mapping both structural and functional brain connections.
Demonstrated effectiveness in capturing brain connectivity relationships.
Showed improved interpretability compared to traditional methods.

Implication

This framework models structural and functional connectivity in the brain, highlighting its effectiveness and interpretability.

Abstract

BC-GTM provides an effectiveness and interpretable GNN framework for advancing research in SC and FC bidirectional mapping.

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Cite This Study

Song et al. (2026) studied this question.

synapsesocial.com/papers/69e5c27e03c2939914028b86 https://doi.org/10.1109/tbme.2026.3685002

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