GRANGER CAUSALITY FOR MIXED TIME SERIES GENERALIZED LINEAR MODELS: A CASE STUDY ON MULTIMODAL BRAIN CONNECTIVITY

• 成果类型: Article
• 署名作者: Piancastelli, Luiza S. C.; Barreto-Souza, Wagner; Fortin, Norbert J.; Cooper, Keiland W.; Ombao, Hernando
• 署名单位: University College Dublin; University of California System; University of California Irvine; University of California System; University of California Irvine; King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/26-AOAS2185
• 发表日期: 2026-06
• 页码: 1541-1561
• 关键词: Bayesian model; brain signals; causality; multimodal data; Time Series; variable selection; sequence; crime

摘要

This paper is motivated by neuroscience studies aimed at understanding causal or predictive interactions between nodes in a brain network using multimodal brain activity data. To assess Granger causality, we introduce a flexible framework through a general class of models that accommodate mixed types of data (binary, count, continuous and positive components) formulated in a generalized linear model (GLM) fashion. To conduct statistical inference for causality, we propose a Bayesian mixed time series model that incorporates spike-and-slab priors on selected parameters. This framework enables effective selection of causal ordering and offers robust uncertainty quantification. The proposed methods are then applied to brain activity data, including both spike train and local field potential (LFP) activity, recorded as animals (rats) performed a complex sequence memory task. The proposed methodology provides critical insights into the causal relationship between band-specific spectral power in the LFP and subsequent spiking activity. Specifically, power in the LFP beta band is predictive of spiking activity 300 milliseconds later, providing a novel analytical tool for this area of emerging interest in neuroscience and demonstrating its usefulness and flexibility in the study of causality in general.