Evidence map›Paper›PMID 38148517›Full record

ArticleEpilepsia2024

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.

Alfredo Lucas, Brittany H Scheid, Akash R Pattnaik, Ryan Gallagher, Marissa Mojena, Ashley Tranquille, Brian Prager, Ezequiel Gleichgerrcht, Ruxue Gong, Brian Litt and 4 more

Open access · greenAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 18 citations in OpenAlex.

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  10. Can electrical stimulation replace spontaneous seizures in epilepsy surgery?medRxiv : the preprint server for health sciences · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Alfredo LucasCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0001-9439-735X
Brittany H ScheidCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-8528-0085
Akash R PattnaikCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-2474-1685
Ryan GallagherCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Marissa MojenaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ashley TranquilleCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Brian PragerCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ezequiel GleichgerrchtDepartment of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA.ORCID https://orcid.org/0000-0002-4212-4146
Ruxue GongDepartment of Neurology, Emory University, Atlanta, Georgia, USA.ORCID https://orcid.org/0000-0003-4708-6414
Brian LittCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Kathryn A DavisCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sandhitsu DasCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Joel M SteinCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nishant SinhaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-2090-4889
University of Pennsylvania · USEmory University · US

Funding

Implementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1M
Institutional Career Development CoreKL2TR002381 · NCATS · EMORY UNIVERSITY · PI HENRY M BLUMBERG, Anandi Nayan Sheth · 2017 to 2026
$14.1M
Neuro HPA Project 1U54AG062334 · NIA · EMORY UNIVERSITY · PI Ighovwerha Ofotokun · 2018 to 2026
$12.2M
Ghost in the Machine: Melding Brain, Computer and BehaviorDP1NS122038 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI LITT, BRIAN · 2020 to 2024
$5.7M
Training Program in Neuroengineering and MedicineT32NS091006 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Brian Litt · 2015 to 2026
$4.1M
Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging ConnectomicsR01NS116504 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2021 to 2025
$3.3M
Guiding epilepsy surgery using network models and Stereo EEGR01NS125137 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2022 to 2025
$2.8M
Virtual Resection to Treat EpilepsyR56NS099348 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2021 to 2021
$552k
NCATS NIH HHS KL2 TR002381NCATS NIH HHS UL1 TR002378NIA NIH HHS U54 AG062334NIH HHS DP1NS122038NINDS NIH HHS DP1 NS122038NINDS NIH HHS R01 NS116504NINDS NIH HHS R01NS116504NINDS NIH HHS R01 NS125137NINDS NIH HHS R56099348NINDS NIH HHS R56 NS099348NINDS NIH HHS T32 NS091006
6 · The paper itself

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.

Indexed as

ElectrocorticographyEpilepsyElectrodesElectrodes, ImplantedElectroencephalographyHumansMagnetic Resonance ImagingProspective StudiesRetrospective Studiesdrug-resistant epilepsyintracranial electrodesmachine learningmultimodalpresurgical planning

Identifiers

PMID38148517
PMCPMC10948311
OpenAlexW4390271927

What OpenQuestion holds

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Registered trials

None linked

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.