Differential Dynamic Causal Nets: Model Construction, Identification and Group Comparisons

TL;DR

This paper introduces a novel method for constructing differential causal networks from EEG data, enabling the analysis of dynamic brain connectivity and disruptions associated with epileptic activity.

Contribution

It presents a new approach using conditionally coupled neuronal circuits and a hierarchical mixed-effects model, along with an evolutionary algorithm for parameter inference.

Findings

Identified network disruptions during epileptic seizures.

Tracked changes in neural connectivity parameters.

Validated method on synthetic and real EEG data.

Abstract

Pathophysiolpgical 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. Here, to address the challenge, we present a novel approach 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 are varying in subjects but follow a mixed-effects model. A novel evolutionary optimisation algorithm for parameter inference in the proposed method is developed using a loss function derived from Chen-Fliess expansions of stochastic differential equations. The method is demonstrated by application to the fitting of coupled Jansen-Rit local models. The performance of the proposed method is evaluated on both synthetic and real EEG data. In the real EEG data analysis, we track changes in the parameters that characterise dynamic causality within brains that demonstrate epileptic activity. We show evidence of network functional disruptions, due to imbalance of excitatory-inhibitory interneurons and altered epileptic brain connectivity, before and during seizure periods.