We are currently developing RAVE-CEZIA (Computational epileptogenic Zone Identification Algorithms), a computational platform funded by the internal Brain Health Institute Pilot Grant (UTMB 2025), designed to analyze and visualize outputs from multiple computational epileptogenic zone (EZ) identification algorithms in patients with drug-resistant epilepsy (DRE). This platform builds upon existing individual R packages, which will be made available through the open-source CRAN repository, and leverages RAVE1—a free, NIH-supported software for high-quality intracranial EEG (iEEG) data analysis and visualization.
The RAVE-CEZIA project will be hosted and managed through the KarasLab GitHub repository, leveraging continuous integration to ensure robust version control and streamlined collaboration among contributors. RAVE1 developed using R Shiny enables cloud-based deployment and web-browser accessibility. RAVE is designed to comply with the iEEG-BIDS standard, promoting interoperability and data harmonization across centers. For data storage, the platform will use the HDF5 format, allowing efficient handling and sharing of large-scale iEEG datasets. The RAVE framework provides a comprehensive end-to-end pipeline, including electrode localization via the YAEL2 module, preprocessing of iEEG signals, and execution of multiple EZ localization algorithms. In addition, RAVE 2.0 integrates advanced signal processing capabilities inspired by EEGLAB, which will be incorporated into RAVE-CEZIA to enhance its analytical power.
RAVE-CEZIA will integrate ten EZ localization algorithms implemented in R, including approaches such as the epileptogenicity index3, phase-locked high gamma4, neural fragility5, fingerprint analysis6, direct current shifts, and high-frequency oscillation (HFO) co-occurrence7 and source estimation using electromagnetic modeling8,9. Furthermore, the platform will incorporate modern machine learning techniques to manage and interpret complex, multimodal datasets. We have preliminary implementations in R for seven published CEZIAs. Of these, five algorithms (Algorithms 2 EZDCHFO, 3 EZEI, 4 EZMultitaper10, 5 EZPLHG, and 6 EZFragility11) are currently available as R packages and can be accessed through a dedicated GitHub repository.
To support and validate the platform, we are assembling a large multicenter dataset, including patients with post-traumatic epilepsy (PTE) following traumatic brain injury (TBI). We hypothesize that systematic validation and optimization of computational EZ identification algorithms (CEZIAs) in this population will enhance the accuracy of EZ localization, improve surgical outcomes, and promote broader clinical adoption of presurgical iEEG evaluation in drug-resistant PTE patients.
| | CEZIA | Features |
| 1 | Fingerprint | Detects the co-occurrence of preictal spikes, high-frequency banding, and low-frequency suppression post-seizure onset. |
| 2 | DC Shift and HFO Co-occurrence | Identifies the two electrodes with the most significant DC shift and HFO features |
| 3 | Epileptogenic Index | Highlights electrodes with the earliest presence of high frequency oscillations in the beta and gamma range relative to the onset of the seizure |
| 4 | FREEZ | Predictive capability of spatiotemporal heatmaps of electrode mean power using multitaper Fourier transform across six frequency ranges |
| 5 | PLHG | Analyzes the relationship between high gamma waves amplitude and low-frequency rhythmic activity (1–25 Hz) during seizures. |
| 6 | Neural fragility | Calculates the extent of imbalance among network nodes, where a high degree of instability within the neural network is associated with the EZ |
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References
1. Magnotti, J. F., Wang, Z. & Beauchamp, M. S. RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. NeuroImage 223, 117341 (2020).
2. Wang, Z., Magnotti, J. F., Zhang, X. & Beauchamp, M. S. YAEL: Your Advanced Electrode Localizer. Open Source Tools and Methods 10, (2023).
3. Bartolomei, F., Chauvel, P. & Wendling, F. Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain 131, 1818–1830 (2008).
4. Weiss, S. A. et al. Seizure localization using ictal phase-locked high gamma: A retrospective surgical outcome study. Neurology 84, 2320–2328 (2015).
5. Li, A. et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci 24, 1465–1474 (2021).
6. Grinenko, O. et al. A fingerprint of the epileptogenic zone in human epilepsies. Brain 141, 117–131 (2018).
7. Izumi, M. et al. Focal ictal direct current shifts by a time constant of 2 seconds were clinically useful for resective epilepsy surgery. Epilepsia 64, 3294–3306 (2023).
8. Gramfort, A., Papadopoulo, T., Olivi, E. & Clerc, M. OpenMEEG: opensource software for quasistatic bioelectromagnetics. BioMed Eng OnLine 9, 45 (2010).
9. Medani, T. et al. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. Neuroimage 267, 119851 (2023).
10. O’Leary, S. et al. Integrating Data Across Oscillatory Power Bands Predicts the Epileptogenic Zone: the Frequency Range Explorer Epileptogenic Zone (FREEZ) Identification Algorithm. 2024.05.31.596825 Preprint at https://doi.org/10.1101/2024.05.31.596825 (2024).
11. Wang, J. & Lesage, A.-C. EZFragility: Compute neural fragility for ictal iEEG time series.