DS004370#

PRIOS

Access recordings and metadata through EEGDash.

Citation: van Blooijs D, Blok S, Huiskamp GJM, Leijten FSS (2022). PRIOS. 10.18112/openneuro.ds004370.v1.0.2

Modality: ieeg Subjects: 7 Recordings: 130 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004370

dataset = DS004370(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 = DS004370(cache_dir="./data", subject="01")

Advanced query

dataset = DS004370(
    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{ds004370,
  title = {PRIOS},
  author = {van Blooijs D and Blok S and Huiskamp GJM and Leijten FSS},
  doi = {10.18112/openneuro.ds004370.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004370.v1.0.2},
}

About This Dataset#

Dataset description

This dataset consists of 6 patients age 13-53 years old where Cortico-Cortical Evoked Potentials (CCEPs) were recorded with Electro-CorticoGraphy (ECoG) during single pulse electrical stimulation (SPES) in the awake patient for clinical routine (SPES-clinical) and under general propofol-anesthesia (SPES-propofol).

For a detailed description see: - The effect of propofol on local effective brain networks (submitted). D. van Blooijs, S. Blok, G.J.M. Huiskamp, P. van Eijsden, H.G.E. Meijer, F.S.S. Leijten

The study was approved by the Medical Ethical Committee from the UMC Utrecht, the Netherlands.

View full README

Dataset description

This dataset consists of 6 patients age 13-53 years old where Cortico-Cortical Evoked Potentials (CCEPs) were recorded with Electro-CorticoGraphy (ECoG) during single pulse electrical stimulation (SPES) in the awake patient for clinical routine (SPES-clinical) and under general propofol-anesthesia (SPES-propofol).

For a detailed description see: - The effect of propofol on local effective brain networks (submitted). D. van Blooijs, S. Blok, G.J.M. Huiskamp, P. van Eijsden, H.G.E. Meijer, F.S.S. Leijten

The study was approved by the Medical Ethical Committee from the UMC Utrecht, the Netherlands.

Contact

Dorien van Blooijs: D.vanBlooijs@umcutrecht.nl
Frans Leijten: F.S.S.leijten@umcutrecht.nl

Data organization

This data is organized according to the Brain Imaging Data Structure specification. A community-driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/

Each patient has their own folder (e.g., sub-PRIOS01 to sub-PRIOS09) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.

Data are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording during the monitoring period. We use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15). The task key-value pair in long-term iEEG recordings describes the patient’s state during the recording of this file. The task label is “SPESclin“ when these files contain data collected during clinical single pulse electrical stimulation (SPES) and “SPESprop” when these files contain data collected during single pulse electrical stimulation (SPES) in the operating room.

Electrode positions were estimated by running Freesurfer on the individual subject MRI scan. All shared electrode positions were converted to MNI305 space using the Freesurfer surface based non-linear transformation. We note that this surface based transformation distorts the dimensions of the grids, but maintains the gyral anatomy.

License

This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/1.0/. We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge by citing the following in any publication. The effect of propofol on local effective brain networks (submitted). D. van Blooijs, S. Blok, G.J.M. Huiskamp, P. van Eijsden, H.G.E. Meijer, F.S.S. Leijten

Code

Code to analyses these data is available at: UMCU-EpiLAB/umcuEpi_PRIOS

Acknowledgements

We thank all patients for participating in this study.

Funding

Research reported in this publication was supported by EpilepsieNL under Award Number NEF17-07 (DvB) and NEF 19-12 (DvB, SB) and the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH122258 (DvB, the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health).

Dataset Information#

Dataset ID	`DS004370`
Title	PRIOS
Year	2022
Authors	van Blooijs D, Blok S, Huiskamp GJM, Leijten FSS
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004370.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: 7
Recordings: 130
Tasks: 2

Channels & sampling rate

Channels: 133 (14), 68 (12), 64 (4)
Sampling rate (Hz): 2048.0
Duration (hours): 0.0

API Reference#

Use the DS004370 class to access this dataset programmatically.

class eegdash.dataset.DS004370(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004370. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 7; recordings: 15; 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/ds004370 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004370

Examples

>>> from eegdash.dataset import DS004370
>>> dataset = DS004370(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