New framework Artemis tackles demographic confounders in neuroimaging

Researchers have developed Artemis, a novel region-level causal framework designed to eliminate demographic confounders in multimodal neuroimaging data. This framework integrates functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data, utilizing graph neural networks (GNNs) to analyze brain networks. Artemis addresses the issue of age and sex systematically influencing connectivity patterns, which can mislead GNNs. By learning region-specific confounder representations, the system allows for causal intervention at each brain region independently, improving the accuracy and interpretability of neuroimaging analyses. AI

IMPACT This research could lead to more accurate AI models for diagnosing and staging neurological conditions by mitigating biases in neuroimaging data.

RANK_REASON The cluster contains a research paper detailing a new methodology for neuroimaging analysis. [lever_c_demoted from research: ic=1 ai=1.0]

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• Alzheimer's Disease Neuroimaging Initiative
• Artemis
• diffusion tensor imaging
• functional magnetic resonance imaging
• graph neural network
• Human Connectome Project
• Oasis

• paper
• other

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