Inferring effective connectivity in epilepsy using dynamic causal modeling

Abstract

This study deals with effective connectivity analysis among distant neural ensembles recorded with intracerebral near field electrodes during seizures in the brain of epileptic patients. Our goal is to analyze the ability of Dynamic Causal Modeling (DCM) approach to detect causal links when the underlying model is a well-known neural population model dedicated to the simulation of epileptic activities in hippocampus. From the state-space description of the system obtained by coupling a pair of such models, a linearization around the equilibrium state leads to a transition matrix and a parametrized description of the power spectral density matrix for the corresponding pair of output ElectroEncephaloGraphic (EEG) signals in the two-population model. Then, the parameters of this global model are estimated in a Bayesian framework from simulated EEG signals by the Expectation Maximization (EM) algorithm, and Log Bayes Factors are employed to discriminate among the possible effective connectivity hypotheses. Simulation results show that DCM can identify and distinguish the independence, unidirectional or bidirectional interactions between two epileptic populations.