Frequency Range Explorer of the Epileptogenic Zone (FREEZ)
Creators
-
O'Leary, Sean
(Producer)1
- Lesage, Anne-Cecile (Researcher)1
- Camarillo-Rodriguez, Liliana (Researcher)1
- Zhou, Oliver (Researcher)1
- Diosely, Silveira (Researcher)1
- Wang, Jiefei (Researcher)1
- A. Sheth, Sameer (Researcher)2
- S. Beauchamp, Michael (Researcher)3
- Wang, Zhengjia (Researcher)3
- F. Magnotti, John (Researcher)3
- J. Karas, Patrick (Project leader)1
Description
Demo Link: https://rave-freez.azurewebsites.net/launcher/
Docker Download and Instructions: https://hub.docker.com/r/karaslab/rave-freez
The "FREEZ" module (Frequency Range Explorer for Epileptogenic Zones) aims to enhance epilepsy treatment by facilitating the identification of the epileptogenic zone (EZ). FREEZ operates within the "Reproducible Analysis and Visualization of iEEG" (RAVE) platform, incorporating advanced visualization tools such as pre-processed signal data displays, multitaper mean power heatmaps, average power over time plots, and power percentile plots to quantitatively track seizure onset. Additionally, the user can interact with time-frequency spectrograms and 3D brain maps. Additionally, we have developed a novel computational approach that analyzes spectral power across six different frequency bands—delta, theta, alpha, beta, gamma, and high gamma—to improve the localization of the EZ. This methodology has been tested on publicly available data and the results, including our trained machine learning model, and the results are accessible via this link: https://doi.org/10.1101/2024.05.31.596825.
Our implementation in the open-source software RAVE ensures reproducibility and enables practitioners to apply this methodology to new datasets, promoting broader usage and ongoing validation of our approach. Here, we have uploaded a Docker image of the RAVE module for users to interact with PT01 (https://openneuro.org/datasets/ds003029/versions/1.0.6) described within our publication for exploration of our methodology. Lauching the docker image on a local device through the Docker desktop application alows for the module to be tested on the users own processed data, or demo data from PT01 provided within the Docker image.
Files
Files
(46.4 GB)
| Name | Size | Download all |
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md5:f51c45d5247f89ce36956f877f6980af
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46.4 GB | Download |
Additional details
Related works
- Is described by
- Preprint: 10.1101/2024.05.31.596825 (DOI)
Software
References
- Magnotti JF, Wang Z, Beauchamp MS. RAVE: comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. NeuroImage (2020) 223:117341.