Title: Bayesian Belief Updating of Spatiotemporal Seizure Dynamics,
Abstract: 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.