Bayesian Belief Updating of Spatiotemporal Seizure Dynamics
This work addresses the need to understand seizure evolution and propagation for physiological and clinical applications, but it is incremental as it builds on prior temporal estimation methods.
The authors tackled the problem of modeling spatiotemporal seizure dynamics by extending a previous temporal estimation method to include spatial propagation using a partial differential equation, and they tested the method on simulated and empirical ECoG data to provide a framework for assimilating these dynamics.
Epileptic seizure activity shows complicated dynamics in both space and time. To understand the evolution and propagation of seizures spatially extended sets of data need to be analysed. We have previously described an efficient filtering scheme using variational Laplace that can be used in the Dynamic Causal Modelling (DCM) framework [Friston, 2003] to estimate the temporal dynamics of seizures recorded using either invasive or non-invasive electrical recordings (EEG/ECoG). Spatiotemporal dynamics are modelled using a partial differential equation -- in contrast to the ordinary differential equation used in our previous work on temporal estimation of seizure dynamics [Cooray, 2016]. We provide the requisite theoretical background for the method and test the ensuing scheme on simulated seizure activity data and empirical invasive ECoG data. The method provides a framework to assimilate the spatial and temporal dynamics of seizure activity, an aspect of great physiological and clinical importance.