Researchers have developed a new framework called CORE to improve the analysis of brain networks from fMRI data, particularly when dealing with data from different sites. This method addresses issues where site-specific biases and averaged connectivity obscure important transient dynamics, hindering generalization. CORE works by decoupling site-specific confounders, extracting a stable population scaffold of connectivity edges, and then modeling transient pathway dynamics on this scaffold. Experiments show CORE significantly outperforms existing methods in cross-site generalization, even with variations in brain parcellation schemes. AI
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IMPACT Enhances cross-site generalization for brain network analysis, potentially improving diagnostic accuracy in multi-center studies.
RANK_REASON This is a research paper detailing a new framework for analyzing fMRI data. [lever_c_demoted from research: ic=1 ai=1.0]