Cross-Scale Causality and Information Transfer in Simulated Epileptic Seizures

An information-theoretic approach for detecting causality and information transfer was applied to phases and amplitudes of oscillatory components related to different time scales and obtained using the wavelet transform from a time series generated by the Epileptor model. Three main time scales and their causal interactions were identified in the simulated epileptic seizures, in agreement with the interactions of the model variables. An approach consisting of wavelet transform, conditional mutual information estimation, and surrogate data testing applied to a single time series generated by the model was demonstrated to be successful in the identification of all directional (causal) interactions between the three different time scales described in the model. Thus, the methodology was prepared for the identification of causal cross-frequency phase-phase and phase-amplitude interactions in experimental and clinical neural data.

Automated summary

An information-theoretic approach was applied to phases and amplitudes of oscillatory components to detect causality and information transfer in simulated epileptic seizures. The study successfully identified all directional interactions between different time scales in the model by utilizing wavelet transform, conditional mutual information estimation, and surrogate data testing. This methodology is now ready for identifying causal cross-frequency phase-phase and phase-amplitude interactions in experimental and clinical neural data.