Develop practical causal modeling to infer phase–amplitude CFC

Develop a practical causal statistical modeling approach that can infer phase–amplitude cross-frequency interactions from neurophysiological time series, enabling model-based estimation of coupling consistent with biophysical mechanisms.

Background

The authors survey analytical frameworks for studying CFC and emphasize the need for biophysically interpretable, generative models to attribute observed cross-frequency relationships to specific mechanisms. While approaches like Dynamic Causal Modeling (DCM) exist for other forms of interactions, the authors note the absence of a practical method for phase–amplitude CFC.

A practical causal modeling framework would enable formal model comparison and parameter inference for mechanisms mediating phase–amplitude interactions, addressing current interpretability limitations of correlation-based CFC measures.