Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation
Summary
We present an electrophysiological dataset collected from the amygdalae of nine subjects attending a visual dynamic stimulation of emotional aversive content. The subjects were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Subjects were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition).
The dataset contains the simultaneous recording of intracranial EEG (iEEG) and neuronal spike times and waveforms, and localization information for iEEG electrodes. Subject characteristics and trial information are provided. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals. This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.
Downloading the data
Using gin
Create an account on gin and download the gin client as described here. On your computer, run the gin-shell. Log in using
gin login
You will be prompted to enter your user name and password.
Change the current path to the directory for downloading the repository.
Clone the repository using:
gin get USZ_NCH/Human_Amygdala_MUA_sEEG_FearVideo
Large data files will not be downloaded automatically. To get them, use
gin get-content <filename>
To download all large files, use
gin get-content .
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_Amygdala_MUA_sEEG_FearVideo.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_Amygdala_MUA_sEEG_FearVideo. 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
Main directory
Contains Subject_Characteristics.pdf which describes subjects and NIX_File_Structure.pdf which describes the structure of the nix files.
Directory code_MATLAB
Contains MATLAB code for loading the data and generating the publication figures. Main_Load_NIX_Data.m contains code snippets for reading NIX data and task related information. Main_Plot_Figures.m uses the functions Figure_2.m and Figure_3.m to generate figures.
Required dependencies to run the script Main_Load_NIX_Data.m:
Required dependencies to run the script Main_Plot_Figures.m:
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
Updates
Updated versions of the data and codes will be provided at:
https://gin.g-node.org/USZ_NCH/.
Support
For questions on the dataset or the task, contact Tommaso Fedele at tofedele@hse.ru.
Related Publications
- Fedele, T., Tzovara, A., Steiger, B., Hilfiker, P., Grunwald, T., Stieglitz, L., Jokeit, H., Sarnthein, J., 2020. The relation between neuronal firing, local field potentials and hemodynamic activity in the human amygdala in response to aversive dynamic visual stimuli. Neuroimage 213, 116705. https://doi.org/10.1016/j.neuroimage.2020.116705
Licensing
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
See LICENSE.txt
for the full license.