DS004998#

Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.

Access recordings and metadata through EEGDash.

Citation: Fayed Rassoulou, Alexandra Steina, Christian J. Hartmann, Jan Vesper, Markus Butz, Alfons Schnitzler, Jan Hirschmann (2024). Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.. 10.18112/openneuro.ds004998.v1.2.2

Modality: meg Subjects: 20 Recordings: 576 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004998

dataset = DS004998(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 = DS004998(cache_dir="./data", subject="01")

Advanced query

dataset = DS004998(
    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{ds004998,
  title = {Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.},
  author = {Fayed Rassoulou and Alexandra Steina and Christian J. Hartmann and Jan Vesper and Markus Butz and Alfons Schnitzler and Jan Hirschmann},
  doi = {10.18112/openneuro.ds004998.v1.2.2},
  url = {https://doi.org/10.18112/openneuro.ds004998.v1.2.2},
}

About This Dataset#

This dataset contains data from externalized DBS patients undergoing simultaneous MEG - STN LFP recordings with (MedOn) and without (MedOn) dopaminergic medication. It has two movement conditions: 1) 5 min of rest followed by static forearm extension (hold) and 2) 5 min of rest followed by self-paced fist-clenching (move). The movement parts contain pauses. Some patients were recorded in resting-state only (rest). The project aimed to understand the neurophysiology of basal ganglia-cortex loops and its modulation by movement and medication.

Code for quickly start is available here: Fayed-Rsl/RHM_preprocessing

References

Rassoulou, F., Steina, A., Hartmann, C. J., Vesper, J., Butz, M., Schnitzler, A., & Hirschmann, J. (2024). Exploring the electrophysiology of Parkinson’s disease with magnetoencephalography and deep brain recordings. Scientific data, 11(1), 889. https://doi.org/10.1038/s41597-024-03768-1

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Dataset Information#

Dataset ID	`DS004998`
Title	Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.
Year	2024
Authors	Fayed Rassoulou, Alexandra Steina, Christian J. Hartmann, Jan Vesper, Markus Butz, Alfons Schnitzler, Jan Hirschmann
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004998.v1.2.2
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: 20
Recordings: 576
Tasks: 8

Channels & sampling rate

Channels: 323 (168), 8 (80), 333 (10), 326 (10), 324 (10), 347 (6), 16 (2), 18 (2), 4, 319
Sampling rate (Hz): 2000.0
Duration (hours): 0.0

API Reference#

Use the DS004998 class to access this dataset programmatically.

class eegdash.dataset.DS004998(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004998. Modality: meg; Experiment type: Motor; Subject type: Parkinson's. Subjects: 20; recordings: 145; tasks: 6.

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/ds004998 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004998

Examples

>>> from eegdash.dataset import DS004998
>>> dataset = DS004998(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