Dataset for mAChR-mediated cholinergic neurotransmission in TMS–EEG responses
Creators
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University of Tübingen
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Université Laval
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Aalto University
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University of Trento
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University of Tehran
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Universitätsklinikum Tübingen
Contributors
Contact person:
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1.
Universitätsklinikum Tübingen
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2.
University of Tübingen
Description
This repository contains a dataset supporting results in the article: Involvement of mAChR-mediated cholinergic neurotransmission in TMS–EEG responses. Song, Y., Gordon, P. C., Roy, O., Metsomaa, J., Belardinelli, P., Rostami, M., & Ziemann, U. in submission, 2024.
The CHODME (PHARMACO) dataset contains pre-processed pharmacologic TMS-EEG data from 24 healthy participants. The raw TMS-EEG data were collected as part of a placebo-controlled cross-over study involving three experimental sessions per participant. Participants were given a single oral dose of scopolamine, biperiden, or placebo during each session, with the drug order randomized and balanced across the sessions. Each session included three blocks of single-pulse TMS–EEG recordings targeting the mPFC, AG, and SMA, respectively.
Table of contents (English)
The dataset includes:
- 24 data files from 24 participants, each including preprocessed tms-eeg signals stored in fieldtrip data structure per cortical target per session. Leadfield matrix per session. Head model per participant.
- An example tms-eeg dataset before preprocessing from a participant (in eeglab data structure). The preprocessing steps are described in the article's method section and in the code: tms_eeg_Cleaningpipeline.m (available in the GitHub repo).
- A generic head model
- Session information
Technical info (English)
The code used to preprocess and process TMS-EEG signal and generate the results in the article can be found on GitHub: https://github.com/Song-Yufei/anti-cholinergic-drugs-on-TMS-EEG-responses.
Other (English)
You need to satisfy these conditions in order for this request to be accepted:
- Please state your name, contact details (e-mail), institution, position, and reason for requesting access to the CHODME(PHARMACO) dataset (the dataset presented in the current repository).
- Please sign the EULA (End User License Agreement), and send us back via email (bnphih@gmail.com) to be granted access to the dataset.
- Once the request is submitted, we will decide on granting the access.
Files
Restricted
The record is publicly accessible, but files are restricted to users with access.
Additional details
Related works
- Continues
- Dataset: 10.5281/zenodo.10004794 (DOI)
- Requires
- Software: https://github.com/Song-Yufei/anti-cholinergic-drugs-on-TMS-EEG-responses (URL)
Data quality
- Accuracy
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The raw TMS-EEG signals were recorded with a TMS-compatible system (NeurOne, Bittium). Electrodes were placed according to the International 10-5 system in an elastic cap (EasyCap BC-TMS-64, EasyCap). EEG was sampled at 5 kHz (device filter DC-1250Hz), and electrode CPz served as the reference online. The raw TMS-EEG data was preprocessed according to a pipeline in 'tms_eeg_Cleaningpipeline.m (available in the GitHub repo)'.
- Completeness
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The dataset includes all the required sessions for 24 participants, with each participant having three sessions.
- Conformity
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The preprocessed TMS-EEG data is stored in a fieldtrip structure and can be loaded into Matlab using scripts in the Github repo.
- Consistency
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The preprocessed TMS-EEG data were processed with the same pipeline 'tms_eeg_Cleaningpipeline.m (available in the GitHub repo)'.
- Credibility
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The pipeline used to preprocess the raw TMS-EEG signal is peer-reviewed and published.
- Processability
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The dataset is stored in the fieldtrip data structure and can be loaded using the script 'load_tmseeg.m (available in the GitHub repo)'.
- Relevance
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The dataset is collected to investigate the the involvement of mAChR-mediated cholinergic neurotransmission in TMS–EEG responses
- Timeliness
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The raw TMS-EEG signals were collected from August 2021 to August 2022.
- Understandability
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A comprehensive description of the dataset, including the detailed experimental protocol, recording parameters, and preprocessing steps etc, is provided in the article.