Club Exco: clustering brain extreme communities from multi-channel EEG data
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866918070657744896 |
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| author | Guerrero, Matheus B. Redondo, Paolo V. Pinto-Orellana, Marco A. Lopour, Beth A. Ombao, Hernando Huser, Raphaël |
| author_facet | Guerrero, Matheus B. Redondo, Paolo V. Pinto-Orellana, Marco A. Lopour, Beth A. Ombao, Hernando Huser, Raphaël |
| contents | Current methods for clustering brain networks over time often rely on cross-dependence measures computed from the entire range of EEG signals, which can obscure information specific to extreme neural activity. To overcome this, we introduce Club Exco, a novel clustering method grounded in extreme value theory, designed to detect brain communities with co-occurring high-amplitude EEG events. By focusing on tail behavior, Club Exco isolates extreme-value synchrony across channels, offering new insights into seizure dynamics. We apply Club Exco to neonatal EEG recordings from 30 patients (13 seizure-free and 17 with clinically confirmed seizures). Our method identifies robust ``brain extreme communities'' and constructs Extreme Connectivity Persistence matrices that summarize how often channels exhibit synchronous extremes across time. Seizure patients exhibit more persistent and variable clustering among non-adjacent regions, suggesting seizure propagation, while non-seizure patients show more consistent clustering in anatomically adjacent regions. Compared to coherence-based methods (e.g., Hierarchical Cluster Coherence procedure), Club Exco captures distinct, seizure-associated connectivity patterns, especially in high-amplitude segments. These results highlight Club Exco's potential to characterize extreme neural events and inform clinical understanding of seizure localization and spread. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_04338 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Club Exco: clustering brain extreme communities from multi-channel EEG data Guerrero, Matheus B. Redondo, Paolo V. Pinto-Orellana, Marco A. Lopour, Beth A. Ombao, Hernando Huser, Raphaël Methodology Applications 60G70, 62H35, 62H30, 62M10, 62P10 G.3 Current methods for clustering brain networks over time often rely on cross-dependence measures computed from the entire range of EEG signals, which can obscure information specific to extreme neural activity. To overcome this, we introduce Club Exco, a novel clustering method grounded in extreme value theory, designed to detect brain communities with co-occurring high-amplitude EEG events. By focusing on tail behavior, Club Exco isolates extreme-value synchrony across channels, offering new insights into seizure dynamics. We apply Club Exco to neonatal EEG recordings from 30 patients (13 seizure-free and 17 with clinically confirmed seizures). Our method identifies robust ``brain extreme communities'' and constructs Extreme Connectivity Persistence matrices that summarize how often channels exhibit synchronous extremes across time. Seizure patients exhibit more persistent and variable clustering among non-adjacent regions, suggesting seizure propagation, while non-seizure patients show more consistent clustering in anatomically adjacent regions. Compared to coherence-based methods (e.g., Hierarchical Cluster Coherence procedure), Club Exco captures distinct, seizure-associated connectivity patterns, especially in high-amplitude segments. These results highlight Club Exco's potential to characterize extreme neural events and inform clinical understanding of seizure localization and spread. |
| title | Club Exco: clustering brain extreme communities from multi-channel EEG data |
| topic | Methodology Applications 60G70, 62H35, 62H30, 62M10, 62P10 G.3 |
| url | https://arxiv.org/abs/2212.04338 |