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README.md

Dataset of intraoperative pre- and post-resection ECoG recorded from epilepsy patients and fast ripple (FR) markings

Summary

We present an electrophysiological dataset recorded from twenty-two subjects during resective epilepsy surgery. We used standard electrodes with 10 mm inter-contact spacing (standard ECoG) in 14 surgeries and high-density grid electrodes with 5 mm spacing (hd-ECoG) in 8 surgeries. We recorded ECoG pre- and post-resection. We detected fast ripples (FR) using a previously validated automatic detector and did visual validation. For each recording, we provide the recorded data and FR markings. We also provide the approximate locations of recordings and resected area. The data was used in our publication "High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome" (doi.org/10.1016/j.clinph.2019.07.008).

Downloading the data

Using gin

Create an account on gin and download the gin client as described here. On your computer, log in using

gin login

Clone the repository using:

gin get USZ_NCH/Intraoperative_ECoG_HFO    

Large data files will not be downloaded automatically. To get them, use

gin get-content <filename>

Downloaded large files will be locked (read-only). You must unlock the files using

gin unlock <filename>

To remove the contents of a large file again, use

gin lock <filename>
gin remove-content <filename>

See here for detailed information on how to use gin.

Using git annex

Make sure git and git-annex are installed on your computer. Create an account on gin and upload your public SSH key to your gin profile. Then clone the repository using

git clone git@gin.g-node.org:/USZ_NCH/Intraoperative_ECoG_HFO .git

Large data files will not be downloaded automatically. To get them, use

git annex get <filename>

Downloaded large files will be locked (read-only). You must unlock the files using

git annex unlock <filename>

To remove the contents of a large file again, use

git annex --force lock <filename>
git annex drop <filename>

See the git annex documentation for details.

Using the web browser

Download the latest release as a zip file by clicking on Releases on the main page at https://gin.g-node.org/USZ_NCH/Intraoperative_ECoG_HFO . This zip file will contain all small (text) files only, while large data files will not be downloaded automatically and an empty placeholder will be put in their place. To get the full content of such a large file , download these files individually as needed from the web interface by clicking on them in the repository browser.

Updates

Updated versions of the codes will be provided at: https://gin.g-node.org/USZ_NCH/Intraoperative_ECoG_HFO

Related Publications

  • Boran E, Ramantani G, Krayenbuhl N, Schreiber M, Konig K, Fedele T, Sarnthein J. High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome. Clin Neurophysiol 2019;130(10):1882-8. https://doi.org/10.1016/j.clinph.2019.07.008.
  • Boran Ece, Sarnthein J, Krayenbühl N, Ramantani G, Fedele T. High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy. Sci Rep.
  • Fedele T, Burnos S, Boran E, Krayenbuhl N, Hilfiker P, Grunwald T, Sarnthein J. Resection of high frequency oscillations predicts seizure outcome in the individual patient. Sci Rep 2017;7(1):13836. https://doi.org/10.1038/s41598-017-13064-1.
  • Fedele T, Ramantani G, Burnos S, Hilfiker P, Curio G, Grunwald T, Krayenbühl N, Sarnthein J. Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram. Clin Neurophysiol 2017;128(7):1220-6. https://doi.org/10.1016/j.clinph.2017.03.038.
  • Fedele T, van 't Klooster M, Burnos S, Zweiphenning W, van Klink N, Leijten F, Zijlmans M, Sarnthein J. Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome. Clin Neurophysiol 2016;127(9):3066-74. https://doi.org/10.1016/j.clinph.2016.06.009.

Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

See LICENSE.txt for the full license.

datacite.yml
Title Dataset of intraoperative pre- and post-resection ECoG recorded from epilepsy patients and fast ripple (FR) markings
Authors Boran,Ece;Klinik für Neurochirurgie, UniversitätsSpital und Universität Zürich, 8091 Zürich, Switzerland;orcid.org/0000-0002-0395-7575
Ramantani,Georgia;Neuropädiatrie, Universitäts-Kinderspital Zürich, 8032 Zürich, Switzerland;orcid.org/0000-0002-7931-2327
Krayenbühl,Niklaus;Klinik für Neurochirurgie, UniversitätsSpital und Universität Zürich, 8091 Zürich, Switzerland
Schreiber,Maxine;Klinik für Neurochirurgie, UniversitätsSpital und Universität Zürich, 8091 Zürich, Switzerland
König,Kristina;Schweizerisches Epilepsie-Zentrum, 8008 Zürich, Switzerland
Fedele,Tommaso;Institute for Cognitive Neuroscience, National Research University Higher School of Economics, 101000 Moscow, Russian Federation;orcid.org/0000-0001-7574-8062
Sarnthein,Johannes;Klinik für Neurochirurgie, UniversitätsSpital und Universität Zürich, 8091 Zürich, Switzerland;orcid.org/0000-0001-9141-381X
Description We present an electrophysiological dataset recorded from twenty-two subjects during resective epilepsy surgery. We used standard electrodes with 10 mm inter-contact spacing (standard ECoG) in 14 surgeries and high-density grid electrodes with 5 mm spacing (hd-ECoG) in 8 surgeries. We recorded ECoG pre- and post-resection. We detected fast ripples (FR) using a previously validated automatic detector and did visual validation. For each recording, we provided the recorded data and FR markings. We also provide the approximate locations of recordings and resected area. The data was used in our publication doi.org/10.1016/j.clinph.2019.07.008.
License Creative Commons Attribution-ShareAlike 4.0 International Public License (https://creativecommons.org/licenses/by-sa/4.0/)
References High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome. [] (IsSupplementTo)
Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram. [] (IsContinuedBy)
High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy. [] (CitedBy)
Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome. [] (Cites)
Resection of high frequency oscillations predicts seizure outcome in the individual patient. [] (Cites)
Funding Swiss National Science Foundation, SNSF 320030_176222
Mach-Gaensslen Stiftung
Stiftung für wissenschaftliche Forschung an der Universität Zürich
Forschungskredit der Universität Zürich
Keywords Epilepsy surgery
Intraoperative recording
Electrocorticography (ECoG)
High frequency oscillations (HFO)
Fast ripple (FR)
Resection area
Seizure outcome
Automatic HFO detection
Electrophysiology
High-density ECoG
Standard ECoG
Spatial sampling
Grid
Strip
Subdural
Epilepsy
Human
Neuroscience
Resource Type Dataset