# 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 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 comprehends the simultaneous recording of intracranial EEG (iEEG) and neuronal spikes timestamps and waveforms, and localization information of iEEG electrode contacts. Subjects characteristics and trial information are provided. We technically validated this dataset and provide here spike sorting quality metric and the spectrum 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](https://web.gin.g-node.org/G-Node/Info/wiki/gin-cli). On your computer, log in using
```bash
gin login
```

Clone the repository using:
```bash
gin get USZ_NCH/Human_Amygdala_MUA_sEEG_FearVideo
```

Large data files will not be downloaded automatically. To get them, use
```bash
gin get-content <filename>
```

Downloaded large files will be locked (read-only). You must unlock the files using
```bash
gin unlock <filename>
```

To remove the contents of a large file again, use
```bash
gin lock <filename>
gin remove-content <filename>
```

See [here](https://web.gin.g-node.org/G-Node/Info/wiki/gin-cli+tutorial) 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
```bash
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
```bash
git annex get <filename>
```

Downloaded large files will be locked (read-only). You must unlock the files using
```bash
git annex unlock <filename>
```

To remove the contents of a large file again, use
```bash
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.xlsx which describes subjects and NIX_File_Structure.xlsx which describes the structure of the nix files.

### Directory code_MATLAB
Contains a MATLAB script (Load_Data_Example_Script.m) with 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

## 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](tofedele@hse.ru). xxxWhich email to use here?

## 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
<a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

See `LICENSE.txt` for the full license.