Bi-cross-validation: a data-driven method to evaluate dynamic functional connectivity models in fMRI
This paper introduces bi-cross-validation as a principled, data-driven framework for evaluating and selecting dynamic functional connectivity models in fMRI, demonstrating its ability to avoid circularity, balance model complexity with goodness-of-fit, and effectively compare static versus dynamic approaches across varying spatial dimensionalities.