iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices.

• Type of Material: Article
• Authors: Alfredo Lucas, University of Pennsylvania; Brittany H Scheid, University of Pennsylvania; Akash R Pattnaik, University of Pennsylvania; Ryan Gallagher, University of Pennsylvania; Marissa Mojena, University of Pennsylvania; Ashley Tranquille, University of Pennsylvania; Brian Prager, University of Pennsylvania; Ezequiel Gleichgerrcht, Emory University; Ruxue Gong, Emory University; Brian Litt, University of Pennsylvania; Kathryn A Davis, University of Pennsylvania; Sandhitsu Das, University of Pennsylvania; Joel M Stein, University of Pennsylvania; Nishant Sinha, University of Pennsylvania
• Language: English
• Date: 2023-06-13
• Publisher: Epilepsia
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: © 1999-2024 John Wiley & Sons, Inc or related companies
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1101/2023.06.12.23291286
• Title of Journal or Parent Work: medRxiv
• Grant/Funding Information: AL and KAD received support from NINDS (R01NS116504). NS received support from American Epilepsy Society (953257) and NINDS (R01NS116504). The authors would also like to thank the Thornton Foundation for their generous support. Brian Litt acknowledges funding from the Pennsylvania Tobacco Fund, NINDS R56099348, NIH DP1NS122038, the Mirowski Family Foundation, Jonathan Rothberg, and Neil and Barbara Smit.
• Keywords: electrode reconstruction; epilepsy; oncology; iEEG
• Research Categories: Biology, Neuroscience; Health Sciences, Oncology; Engineering, Biomedical

Abstract

BACKGROUND: Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive 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. These tasks are still performed manually in many epilepsy centers. 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. METHODS: We created IEEG-recon , a scalable electrode reconstruction pipeline for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: 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. RESULTS: We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ∼20 min for semi-automatic electrode labeling. 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 post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation. DISCUSSION: iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.