Robust characterization of dynamic causal interactions inmultivariate biomedical signals is essential for advancingcomputational and algorithmic methods in biomedical imaging. Conventional approaches, such as Dynamic Bayesian Networks (DBNs), often assume linear or simple statistical dependencies, while manifold-based techniques like Convergent Cross Mapping (CCM) capture nonlinear, laggedinteractions but lack probabilistic quantification and interventional modeling. We introduce a DBN-informed CCM framework that integrates geometric manifold reconstruction with probabilistic temporal modeling. Applied to multimodal EEG–EMG recordings from dystonic and neurotypical children, the method quantifies uncertainty, supports interventional simulation, and reveals distinct frequency-specific reorganization of cortico-muscular pathways in dystonia. Experimental results show marked improvements in predictiveconsistency and causal stability as compared to baselineapproaches, demonstrating the potential of causality-awaremultimodal modeling for developing quantitative biomarkersand guiding targeted neuromodulatory interventions.
Abbas et al. (Thu,) studied this question.