DS003568: meg dataset, 51 subjects#
Mood induction in MDD and healthy adolescents
Citation: Lucrezia Liuzzi, Katharine Chang, Hanna Keren, Charles Zheng, Dipta Saha, Dylan Nielson, Argyris Stringaris (20). Mood induction in MDD and healthy adolescents. 10.18112/openneuro.ds003568.v1.0.2
51-participant MEG dataset — Mood induction in MDD and healthy adolescents.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003568
dataset = DS003568(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003568(cache_dir="./data", subject="01")
Advanced query
dataset = DS003568(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds003568,
title = {Mood induction in MDD and healthy adolescents},
author = {Lucrezia Liuzzi and Katharine Chang and Hanna Keren and Charles Zheng and Dipta Saha and Dylan Nielson and Argyris Stringaris},
doi = {10.18112/openneuro.ds003568.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003568.v1.0.2},
}
About This Dataset#
This dataset contains the MEG and structural MRI data from the “Electrophysiological correlates of mood and reward dynamics in human adolescents” pre-registered analysis (https://www.biorxiv.org/content/10.1101/2021.03.04.433969v1).
The task-mmi3 data corresponds to the monetary gambling mood induction task described in the paper. Task-mmi3 data has been pre-processed marking bad channels and bad segments (motion > 5mm or/and noise artifacts).
Task-rest data is unprocessed 10 minutes resting state scan acquired during the same scanning session.
Anatomical MRIs have been defaced and co-registered fiducial coordinates are available in the anatomical json files. Data from four confirmatory subjects are not made available because of missing data sharing consent. sub-22658 and sub-24247 do not have an available structural scan.
Cohort#
Dataset Statistics#
Sex composition
Channel counts (ch)
Sampling frequencies: 1200.0 Hz (n=118 recordings)
Total recording duration: 22 h 32 min
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Mood induction in MDD and healthy adolescents |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Lucrezia Liuzzi, Katharine Chang, Hanna Keren, Charles Zheng, Dipta Saha, Dylan Nielson, Argyris Stringaris |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003568,
title = {Mood induction in MDD and healthy adolescents},
author = {Lucrezia Liuzzi and Katharine Chang and Hanna Keren and Charles Zheng and Dipta Saha and Dylan Nielson and Argyris Stringaris},
doi = {10.18112/openneuro.ds003568.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003568.v1.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003568 · Liuzzi2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003568(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Mood induction in MDD and healthy adolescents
- Study:
ds003568(OpenNeuro)- Author (year):
Liuzzi2021- Canonical:
—
Also importable as:
DS003568,Liuzzi2021.Modality:
meg; Experiment type:Affect; Subject type:Healthy. Subjects: 51; recordings: 118; tasks: 2.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds003568 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003568 DOI: https://doi.org/10.18112/openneuro.ds003568.v1.0.2 NEMAR citation count: 4
Examples
>>> from eegdash.dataset import DS003568 >>> dataset = DS003568(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003568").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds003568 to reproduce the tutorial on this dataset.
Citation
Lucrezia Liuzzi, Katharine Chang, Hanna Keren, Charles Zheng, Dipta Saha, … (20). Mood induction in MDD and healthy adolescents. 10.18112/openneuro.ds003568.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003568.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset