arXiv:2606.18287v1 Announce Type: new Abstract: Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural networks. However, demographic factors such as age and sex systematically confound the relationship between brain connectivity and clinical outcomes, causing GNNs to exploit spurious shortcuts rather than learning causally invariant representations. While recent causal GNN methods introduce causality at the graph-modeling level, their causal mechanisms remain domain-agnosti
The proliferation of multimodal neuroimaging and advanced AI models like GNNs necessitates robust methods to mitigate confounding variables and ensure reliable causal inference in medical AI applications.
Developing causal GNNs for neuroimaging is crucial for advancing AI's utility in clinical diagnostics and treatment, ensuring that AI interpretations are based on actual biological mechanisms rather than spurious correlations.
This research introduces a novel approach to build more robust and causally invariant AI models for analyzing brain connectivity, improving the trustworthiness and effectiveness of AI in medical research and applications.
- · Medical AI researchers
- · Neuroscience
- · Healthcare providers
- · Patients needing advanced diagnostics
- · AI models without causal reasoning
- · Approaches relying on spurious correlations in medical data
Improved accuracy and reliability of AI-driven neuroimaging diagnostics and prognostics.
Accelerated discovery of biomarkers and therapeutic targets for neurological and psychological disorders.
Potential for an increased regulatory focus on causal interpretability in AI systems deployed in high-stakes fields like medicine.
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Read at arXiv cs.LG