DS004837#

Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis

Access recordings and metadata through EEGDash.

Citation: Fran López-Caballero, Mark Curtis, Brian Coffman, Dean Salisbury (2023). Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis. 10.18112/openneuro.ds004837.v1.0.2

Modality: meg Subjects: 60 Recordings: 106 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004837

dataset = DS004837(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 = DS004837(cache_dir="./data", subject="01")

Advanced query

dataset = DS004837(
    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{ds004837,
  title = {Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis},
  author = {Fran López-Caballero and Mark Curtis and Brian Coffman and Dean Salisbury},
  doi = {10.18112/openneuro.ds004837.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004837.v1.0.2},
}

About This Dataset#

Project Title: Pittsburgh Early Psychosis Program (PEPP): Mismatch Negativity (MMN) in First-Episode Psychosis Expected experimentation period: Start date: 12/01/2017 End date: 09/15/2021 Project Description: Oddball paradigm with standards, pitch deviants and duration deviants. Extended description at: https://doi.org/10.1111/ejn.16107 Participant categories: Healthy controls (HC), First-episode Schizophrenia (FESZ), First-episode Affective Psychosis (FEAFF) Further information about clinical, neuropsychological, demographic and medication data can be found in derivatives/participants.csv Events: 1: standard 2: pitch deviant 3: duration deviant Funding: National Institutes of Health (R01 MH108568 and MH113533)

Dataset Information#

Dataset ID	`DS004837`
Title	Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis
Year	2023
Authors	Fran López-Caballero, Mark Curtis, Brian Coffman, Dean Salisbury
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004837.v1.0.2
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: 60
Recordings: 106
Tasks: 1

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): 3000.0 (98), 1000.0 (8)
Duration (hours): 0.0

API Reference#

Use the DS004837 class to access this dataset programmatically.

class eegdash.dataset.DS004837(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004837. Modality: meg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. Subjects: 60; recordings: 106; 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/ds004837 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004837

Examples

>>> from eegdash.dataset import DS004837
>>> dataset = DS004837(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