ArticleNetwork neuroscience (Cambridge, Mass.)2020
The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG.
Article in Network neuroscience (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Influences of electrode density on intracranial seizure localisation: a single-blinded randomised crossover study.EBioMedicine · 2025Trial
- The seizure embedding map: a spatio-temporal transformer for comparing patients by ictal intracranial EEG features at scale.Journal of neural engineering · 2026Article
- 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
- Mesial-to-lateral gradients of epileptiform activity to localize mesial temporal lobe epilepsy.Epilepsia · 2025Article
- Quantifying hubness to predict surgical outcomes in epilepsy: Assessing resection-hub alignment in interictal intracranial EEG networks.Epilepsia · 2024Article
- Network excitability of stimulation-induced spectral responses helps localize the seizure onset zone.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- Utility of intracranial EEG networks depends on re-referencing and connectivity choice.Brain communications · 2024Article
- The seizure severity score: a quantitative tool for comparing seizures and their response to therapy.Journal of neural engineering · 2023Article
- The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence.Brain : a journal of neurology · 2023Article
- Quantitative approaches to guide epilepsy surgery from intracranial EEG.Brain : a journal of neurology · 2023Article
- Article
- The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives.Frontiers in network physiology · 2023Review
- Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG.Brain connectivity · 2022Review
- Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning.Journal of neural engineering · 2022Article
- Normative intracranial EEG maps epileptogenic tissues in focal epilepsy.Brain : a journal of neurology · 2022Article
- On the prediction of neuronal microscale topology descriptors based on mesoscale recordings.The European journal of neuroscience · 2021Article
- Time-evolving controllability of effective connectivity networks during seizure progression.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models.Brain communications · 2021Article
- Interictal intracranial electroencephalography for predicting surgical success: The importance of space and time.Epilepsia · 2020Article
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Authors and funding
10 authors.
Funding
Abstract
Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.
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