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

Susanne Ditlevsen, Irene Tubikanec, Massimiliano Tamborrino

Publication date: 27 June 2023

Abstract: In this article, we propose a 6N-dimensional stochastic differential equation (SDE), modelling the activity of N coupled populations of neurons in the brain. This equation extends the Jansen and Rit neural mass model, which has been introduced to describe human electroencephalography (EEG) rhythms, in particular signals with epileptic activity. Our contributions are threefold: First, we introduce this stochastic N-population model and construct a reliable and efficient numerical method for its simulation, extending a splitting procedure for one neural population. Second, we present a modified Sequential Monte Carlo Approximate Bayesian Computation (SMC-ABC) algorithm to infer both the continuous and the discrete model parameters, the latter describing the coupling directions within the network. The proposed algorithm further develops a previous reference-table acceptance rejection ABC method, initially proposed for the inference of one neural population. On the one hand, the considered SMC-ABC approach reduces the computational cost due to the basic acceptance-rejection scheme. On the other hand, it is designed to account for both marginal and coupled interacting dynamics, allowing to identify the directed connectivity structure. Third, we illustrate the derived algorithm on both simulated data and real multi-channel EEG data, aiming to infer the brain's connectivity structure during epileptic seizure. The proposed algorithm may be used for parameter and network estimation in other multi-dimensional coupled SDEs for which a suitable numerical simulation method can be derived.

Has companion code repository: https://github.com/irenetubikanec/networkabc

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