CONEX-CONNECT: LEARNING PATTERNS IN EXTREMAL BRAIN CONNECTIVITY FROM MULTICHANNEL EEG DATA

• 成果类型: Article
• 署名作者: Guerrero, Matheus B.; Huser, Raphael; Ombao, Hernando
• 署名单位: King Abdullah University of Science & Technology
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1621
• 发表日期: 2023
• 页码: 178-198
• 关键词: time-series; dependence; statistics; models

摘要

Epilepsy is a chronic neurological disorder; it affects more than 50 million people globally. An epileptic seizure acts like a temporary shock to the neuronal system, disrupting normal electrical activity in the brain. Epilepsy is frequently diagnosed with electroencephalograms (EEGs). Current methods study only the time-varying spectra and coherence but do not directly model changes in extreme behavior, neglecting the fact that neuronal oscillations exhibit non-Gaussian heavy-tailed probability distributions. To overcome this limitation, we propose a new approach to characterize brain connectivity based on the joint tail (i.e., extreme) behavior of the EEGs. Our proposed method, the conditional extremal dependence for brain connectivity (Conex- Connect), is a pioneering approach that links the association between extreme values of higher oscillations at a reference channel with the other brain network channels. Using the Conex-Connect method, we discover changes in the extremal dependence driven by the activity at the foci of the epileptic seizure. Our model-based approach reveals that, preseizure, the dependence is notably stable for all channels when conditioning on extreme values of the focal seizure area. By contrast, the dependence between channels is weaker during the seizure, and dependence patterns are more chaotic. Using the Conex-Connect method, we identified the high-frequency oscillations as the most relevant features, explaining the conditional extremal dependence of brain connectivity.