DS005356#

MEG: Major Depression & Probabilistic Learning Task

Access recordings and metadata through EEGDash.

Citation: [Unspecified] (2024). MEG: Major Depression & Probabilistic Learning Task. 10.18112/openneuro.ds005356.v1.5.0

Modality: meg Subjects: 85 Recordings: 630 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005356

dataset = DS005356(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005356(cache_dir="./data", subject="01")

Advanced query

dataset = DS005356(
    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{ds005356,
  title = {MEG: Major Depression & Probabilistic Learning Task},
  author = {[Unspecified]},
  doi = {10.18112/openneuro.ds005356.v1.5.0},
  url = {https://doi.org/10.18112/openneuro.ds005356.v1.5.0},
}

About This Dataset#

Howdy y’all. Here’s data from:

Pirrung, C.J.H., Singh G., Hogeveen, J., Quinn, D. & Cavanagh, J.F. (2025) Hypoactivation of ventromedial frontal cortex in major depressive disorder: an MEG study of the Reward Positivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

An MEG study (306-sensor Elekta Neuromag System) of the Reward Positivity during reinforcement learning. Participants were all SCID interviewed to meet either control (CTL, non-depressed, n=38) or major depressive disorder (MDD, n=52) group criteria. Task was an MEG-compatible probabilistic selection task. We’ll upload their T1s and resting state soon. <jcavanagh@unm.edu>

Dataset Information#

Dataset ID

DS005356

Title

MEG: Major Depression & Probabilistic Learning Task

Year

2024

Authors

[Unspecified]

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005356.v1.5.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005356,
  title = {MEG: Major Depression & Probabilistic Learning Task},
  author = {[Unspecified]},
  doi = {10.18112/openneuro.ds005356.v1.5.0},
  url = {https://doi.org/10.18112/openneuro.ds005356.v1.5.0},
}

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: 85

  • Recordings: 630

  • Tasks: 1

Channels & sampling rate

  • Channels: 396 (173), 71 (53), 450 (2), 125 (2)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Depression

  • Modality: Visual

  • Type: Learning

Files & format

  • Size on disk: 161.6 GB

  • File count: 630

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005356.v1.5.0

Provenance

API Reference#

Use the DS005356 class to access this dataset programmatically.

class eegdash.dataset.DS005356(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005356. Modality: meg; Experiment type: Learning; Subject type: Depression. Subjects: 85; recordings: 116; tasks: 1.

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/ds005356 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005356

Examples

>>> from eegdash.dataset import DS005356
>>> dataset = DS005356(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

See Also#