A1933
Title: Dynamic causal modelling and spectral analysis
Authors: Jian Zhang - University of Kent (United Kingdom) [presenting]
Abstract: Pathophysiological modelling of brain systems from microscale to macroscale remains difficult in group comparisons partly because of the infeasibility of modelling the interactions of thousands of neurons at the scales involved. To address this challenge, a novel approach is presented to construct differential causal networks directly from electroencephalogram (EEG) data. The proposed network is based on conditionally coupled neuronal circuits which describe the average behaviour of interacting neuron populations that contribute to observed EEG data. In the network, each node represents a parameterised local neural system while directed edges stand for node-wise connections with transmission parameters. The network is hierarchically structured in the sense that node and edge parameters vary across subjects but follow a mixed-effects model. Spectral power analysis is further conducted. The method is used to identify dynamic causal networks and differential power regions for epilepsy.
