Abstract
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.
Funding Statement
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. OSR-CRG2017-3434 and No. OSR-CRG2020-4394.
Acknowledgments
The authors would like to thank the Editor, Associate Editor, and two referees for valuable suggestions that have improved the manuscript. The EEG dataset was collected by Dr. Beth Malow, attending neurologist from the University of Michigan Hospital, and made available by the authors of the paper Ombao et al. (2001).
Citation
Matheus B. Guerrero. Raphaël Huser. Hernando Ombao. "Conex–Connect: Learning patterns in extremal brain connectivity from MultiChannel EEG data." Ann. Appl. Stat. 17 (1) 178 - 198, March 2023. https://doi.org/10.1214/22-AOAS1621
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