ArticleEpilepsia2024
iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.
Article in Epilepsia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 18 citations in OpenAlex.
- The seizure embedding map: a spatio-temporal transformer for comparing patients by ictal intracranial EEG features at scale.Journal of neural engineering · 2026Article
- High-frequency oscillations after acute hemorrhagic traumatic brain injury: insights into posttraumatic epilepsy development.Epilepsia · 2026Article
- Virtual Responsive Neurostimulation Implantation: From Intracranial Connectivity to Optimized Lead Placement.medRxiv : the preprint server for health sciences · 2026Article
- A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.Scientific data · 2026Article
- Regional excitability, not epileptic pathology, drives stimulation-evoked interictal spike increases.medRxiv : the preprint server for health sciences · 2026Article
- Intrinsic and extrinsic connectivity of the seizure onset zone at rest and during stimulation.medRxiv : the preprint server for health sciences · 2026Article
- Multi-expert consensus annotations of spontaneous and stimulation-induced seizures in stereotactic EEG.medRxiv : the preprint server for health sciences · 2026Article
- EStiMapp: A practical tool for mapping clinical symptoms evoked by electrical stimulation in intracranial EEG for epilepsy surgery.Clinical neurophysiology practice · 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
- Can electrical stimulation replace spontaneous seizures in epilepsy surgery?medRxiv : the preprint server for health sciences · 2025Article
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Stereo-EEG propagating source reconstruction identifies new surgical targets for epilepsy patients.Brain : a journal of neurology · 2025Article
- From data to decision: Scaling artificial intelligence with informatics for epilepsy management.Clinical and translational medicine · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- The sixth sense: how much does interictal intracranial EEG add to determining the focality of epileptic networks?Brain communications · 2024Article
- Utility of intracranial EEG networks depends on re-referencing and connectivity choice.Brain communications · 2024Article
- YAEL: Your Advanced Electrode Localizer.eNeuro · 2023Article
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveClinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.
methodsWe created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.
resultsWe used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. SIGNIFICANCE: iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.