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

2026-01-29

AI Summary

This study addresses the challenge of constructing interpretable dynamic causal networks from electroencephalography (EEG) data for group-level comparisons. The authors propose a novel paradigm that builds differential dynamic causal networks based on the parametrized Jansen-Rit local neural mass model, where nodes represent local neural circuits and directed edges encode causal connections with conduction delays. A loss function derived via the Chen–Fliess expansion is combined with a mixed-effects modeling framework to account for inter-subject variability, and a new evolutionary optimization algorithm is designed for efficient parameter inference. Experiments on both synthetic and real epileptic EEG datasets demonstrate that the method effectively quantifies inter-group differences in neural dynamics, successfully identifying excitatory–inhibitory imbalances and aberrant functional connectivity associated with seizure onset and offset.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Causal Learning
• Search and Optimization: Evolutionary Computation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• pathophysiological modelling
• group comparisons
• neuronal interactions
• EEG data
• dynamic causality

Innovation

• Differential Dynamic Causal Nets
• Evolutionary Optimization
• Mixed-effects Model
• Chen-Fliess Expansion
• EEG-based Causal Inference