Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task
Summary
We present an electrophysiological dataset recorded from nine subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10-20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms and spike times of 1526 units recorded in the medial temporal lobe, anonymized MRI and CT scans, and the MNI coordinates of all intracranial electrodes. Patient characteristics and information on sessions (set size, match/mismatch, correct/incorrect, response, response time for each trial) are also provided. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, alongside hard to obtain unit recordings from humans. Moreover, the MRI and CT scans can be used to build subject-specific head models that include intracranial electrodes. Simultaneous EEG and iEEG recordings, along with the head model, can be used to validate source localization methods.
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/Human_MTL_units_scalp_EEG_and_iEEG
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/Human_MTL_units_scalp_EEG_and_iEEG.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/Human_MTL_units_scalp_EEG_and_iEEG. 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.
Repository structure
Directory code_MATLAB
Contains a MATLAB script that contains code snippets to read data and task related information.
Directory data_nix
Contains nix files for each session of the task. Each file is named with the format:
Data_Subject_<subject number>_Session_<session number>.h5
Directory data_imaging
Contains post implantation structural T1-weighted MRI and CT scans in the ACPC coordinate system for each subject.
Each MRI file is named with the format: MRI_Defaced_Subject_<subject number>.nii
Each CT file is named with the format: CT_Subject_<subject number>.nii
Updates
Updated versions of the codes will be provided at:
https://gin.g-node.org/USZ_NCH/Human_MTL_units_scalp_EEG_and_iEEG
Related Publications
- Boran E, Fedele T, Klaver P, Hilfiker P, Stieglitz L, Grunwald T, Sarnthein J. Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load. Sci Adv 2019;5(3):eaav3687. http://doi.org/10.1126/sciadv.aav3687.
Licensing
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
See LICENSE.txt for the full license.
Ece Boran