Building an Ecosystem of Seizure Localization Methods: Neural Fragility as the First Step

Abstract

The current treatment for drug-resistant epilepsy (DRE) is surgical intervention, which relies on accurate identification of the seizure onset zone (SOZ) using intracranial EEG (iEEG) data. iEEG analysis with computational epileptogenic zone identification algorithms (CEZIAs) is a promising step towards better SOZ localization and surgical outcomes. A key step in validation and adoption of CEZIAs is to allow for widespread shared development and validation of code and data. We describe a set of three R packages to achieve this goal. Our ecosystem of seizure localization methods involves a straightforward analysis pipeline, standardized data formatting and storage, and completely documented and open-source code. The TableContainer package allows for easy storage and manipulation of table data, serving as groundwork for the Epoch package, which is specifically geared towards iEEG data. The Epoch package allows for cropping, resampling, and visualization of iEEG data and provides publicly downloadable iEEG data for reproducibility. Finally, the EZFragility package uses these two foundational packages to analyze iEEGs for SOZ localization using the Neural Fragility method described by Li et al. EZFragility was built using the same core principles as the original method but included several enhancements in computational efficiency and user experience. It accurately reproduces neural fragility results for both sample patients used in the original paper. This project serves as the first step towards building an open-source, reproducible ecosystem of seizure localization methods in R. Future steps include the addition of other CEZIAs using the framework and sample data already made available by these packages. Significance StatementLocalization of the seizure onset zone (SOZ) is a critical step in surgical treatment of drug-resistant epilepsy. Computational epileptogenic zone identification algorithms (CEZIAs) are promising potential tools to aid in clinical decision-making. However, shared development and verification of CEZIAs is difficult due to obscured source code, variable data structures, and limited data availability. The EZFragility software package1 is the first step in building a collaborative ecosystem of CEZIAs that can be downloaded, tested, and used without these roadblocks. EZFragility1 and its dependent packages TableContainer2 and Epoch3 are freely available on the Comprehensive R Archive Network (CRAN), with source code viewable on GitHub. They provide an open-source framework for CEZIA code, data formatting, and data access, all with extensive documentation.