Authors :
Presenting Author: Brittany Scheid, PhD – Cadence Neuroscience
Alfreado Lucas, BS – University of Pennsylvania; Akash Pattnaik, BS – University of Pennsylvania; Ryan Gallagher, BS – University of Pennsylvania; Marissa Mohena, BS – University of Pennsylvania; Ashley Tranquille, BS – University of Pennsylvania; Brian Prager, PhD – University of Pennsylvania; Ezequiel Gleichgerrcht, MD, PHD – Medical University of South Carolina; Ruxue Gong, PHD Student – Emory; Brian Litt, MD – University of Pennsylvania; Kathryn Davis, MD – University of Pennsylvania; Sandhitsu Das, PHD – University of Pennsylvania; Joel Stein, MD, PHD – University of Pennsylvania; Nishant Sinha, PhD – University of Pennyslvania
Rationale:
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 wanted to satisfy the need for a standalone, modular pipeline that performs electrode reconstruction, is compatible with clinical and research workflows, and can be scaled on cloud platforms.
Methods:
We created iEEG-recon, a scalable electrode reconstruction pipeline that integrates state-of-the-art tools for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three core modules: two clinical modules 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 runtime on the order of 10 minutes per case. 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. The faster AntsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely-used Freesurfer segmentation. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io.Conclusions:
iEEG-recon is a valuable, scalable tool for automating reconstruction of iEEG electrodes on brain MRI, promoting efficient data analysis, and integrating into clinical workflows. iEEG-recon significantly reduces the time and technical barriers to electrode reconstruction and localization, making it a useful resource for all levels of epilepsy centers and research expertise. Funding: Support for this work comes from the Mirowski Foundation (BS), NINDS (R01NS116504) (AL and KAD), the American Epilepsy Society (953257) and NINDS (R01NS116504) (NS).