ArticleBrain communications2024
Utility of intracranial EEG networks depends on re-referencing and connectivity choice.
Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Robust time delay estimation for measurement of ictal EEG traveling waves.Journal of neural engineering · 2026Article
- The seizure embedding map: a spatio-temporal transformer for comparing patients by ictal intracranial EEG features at scale.Journal of neural engineering · 2026Article
- Distance of bipolar re-referencing imparts nonlinear frequency-specific influences on intracranial recording signal measurements.Journal of neural engineering · 2025Article
- The Seizure Embedding Map: A Spatio-Temporal Transformer for Comparing Patients by Ictal Intracranial EEG Features at Scale.medRxiv : the preprint server for health sciences · 2025Article
- Can brain network analyses guide epilepsy surgery?Current opinion in neurology · 2025Review
- Validating MEG estimated resting-state connectome with intracranial EEG.Network neuroscience (Cambridge, Mass.) · 2025Article
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14 authors.
Funding
Abstract
Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test
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