DS003568#

Mood induction in MDD and healthy adolescents

Access recordings and metadata through EEGDash.

Citation: Lucrezia Liuzzi, Katharine Chang, Hanna Keren, Charles Zheng, Dipta Saha, Dylan Nielson, Argyris Stringaris (2021). Mood induction in MDD and healthy adolescents. 10.18112/openneuro.ds003568.v1.0.4

Modality: meg Subjects: 51 Recordings: 3707 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds003568.v1.0.4},
}

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.

Dataset Information#

Dataset ID	`DS003568`
Title	Mood induction in MDD and healthy adolescents
Year	2021
Authors	Lucrezia Liuzzi, Katharine Chang, Hanna Keren, Charles Zheng, Dipta Saha, Dylan Nielson, Argyris Stringaris
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003568.v1.0.4
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 51
Recordings: 3707
Tasks: 2

Channels & sampling rate

Channels: 340 (48), 339 (29), 335 (11), 336 (6), 342 (5), 343 (5), 338 (3), 309 (3), 312 (3), 305 (2), 348, 313, 310
Sampling rate (Hz): 1200.0
Duration (hours): 0.0

API Reference#

Use the DS003568 class to access this dataset programmatically.

class eegdash.dataset.DS003568(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003568. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None