NETWORK INFERENCE VIA APPROXIMATE BAYESIAN COMPUTATION. ILLUSTRATION ON A STOCHASTIC MULTIPOPULATION NEURAL MASS MODEL

• 成果类型: Article
• 署名作者: Itlevsen, Usanne; Amborrino, Assimiliano; Ubikanec, Rene
• 署名单位: University of Copenhagen; University of Warwick; Johannes Kepler University Linz
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2084
• 发表日期: 2025-12
• 页码: 3304-3329
• 关键词: Simulation-based inference; network inference; approximate Bayesian computation; numerical splitting methods; Jansen and Rit neural mass model; EEG data; parameter-estimation

摘要

In this article we propose an adapted sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm for network inference in coupled stochastic differential equations (SDEs) used for multivariate time series modeling. Our approach is motivated by neuroscience, specifically the challenge of estimating brain connectivity before and during epileptic seizures. To this end, we make four key contributions. First, we introduce a 6N-dimensional SDE to model the activity of N coupled neuronal populations, extending the (single-population) stochastic Jansen and Rit neural mass model used to describe human electroencephalography (EEG) rhythms, particularly epileptic activity. Second, we construct a reliable and efficient numerical splitting scheme for the model simulation. Third, we apply the proposed adapted SMC-ABC algorithm to the neural mass model and validate it on different types of simulated data. Compared to standard SMC-ABC, our approach significantly reduces computational cost by requiring fewer model simulations to reach the desired posterior region, thanks to the inclusion of binary parameters describing the presence or absence of coupling directions. Finally, we apply our method to real multichannel EEG data, uncovering potential similarities in patients' brain activities across different epileptic seizures as well as differences between preseizure and seizure periods.