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The aim of this article is to infer the connectivity structures of brainregions before and during epileptic seizure. Our contributions are fourfold.First, we propose a 6N-dimensional stochastic differential equation formodelling the activity of N coupled populations of neurons in the brain. Thismodel further develops the (single population) stochastic Jansen and Rit neuralmass model, which describes human electroencephalography (EEG) rhythms, inparticular signals with epileptic activity. Second, we construct a reliable andefficient numerical scheme for the model simulation, extending a splittingprocedure proposed for one neural population. Third, we propose an adaptedSequential Monte Carlo Approximate Bayesian Computation algorithm forsimulation-based inference of both the relevant real-valued model parameters aswell as the {0,1}-valued network parameters, the latter describing the couplingdirections among the N modelled neural populations. Fourth, after illustratingand validating the proposed statistical approach on different types ofsimulated data, we apply it to a set of multi-channel EEG data recorded beforeand during an epileptic seizure. The real data experiments suggest, forexample, a larger activation in each neural population and a strongerconnectivity on the left brain hemisphere during seizure.

Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation