DS004457#

Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex

Access recordings and metadata through EEGDash.

Citation: Harvey Huang, Nicholas M Gregg, Gabriela Ojeda Valencia, Benjamin H Brinkmann, Brian N Lundstrom, Gregory A Worrell, Kai J Miller, Dora Hermes (2023). Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex. 10.18112/openneuro.ds004457.v1.0.1

Modality: ieeg Subjects: 5 Recordings: 5 License: CC0 Source: openneuro Citations: 3.0

Metadata: Good (80%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004457

dataset = DS004457(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 = DS004457(cache_dir="./data", subject="01")

Advanced query

dataset = DS004457(
    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{ds004457,
  title = {Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex},
  author = {Harvey Huang and Nicholas M Gregg and Gabriela Ojeda Valencia and Benjamin H Brinkmann and Brian N Lundstrom and Gregory A Worrell and Kai J Miller and Dora Hermes},
  doi = {10.18112/openneuro.ds004457.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004457.v1.0.1},
}

About This Dataset#

Basis Profile Curve identification in the human ventral temporal cortex

This dataset contains intracranial EEG recordings from five patients during single pulse electrical stimulation as described in: * H Huang, NM Gregg, G Ojeda Valencia, BH Brinkmann, BN Lundstrom, GA Worrell, KJ Miller, and D Hermes (2022) Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex. (Under Review)

Please cite this work when using the data. These data were recorded at the Mayo Clinic in Rochester, MN, as part of the NIH Brain Initiative supported project R01 MH122258 “CRCNS: Processing speed in the human connectome across the lifespan”. Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH122258 and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM065841. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The data was collected by Harvey Huang, Dora Hermes, Nick Gregg, Brian Lundstrom, Cindy Nelson, Gregg Worrell and Kai J. Miller. The BIDS formatting was performed by Harvey Huang, Dora Hermes and Gabriela Ojeda Valencia. Data can be analyzed using the Matlab code at: * hharveygit/VTCBPC_JNS_Manu

Format

Data are formatted according to BIDS version 1.9.9

Single pulse stimulation

The patient were resting in the hospital bed, while single pulse stimulation was performed with a frequency of ~0.2 Hz. The stimulation had a duration of 200 microseconds, was biphasic and had an amplitude of 6mA.

Contact

Please contact Dora Hermes (hermes.dora@mayo.edu) for questions.

Dataset Information#

Dataset ID	`DS004457`
Title	Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex
Year	2023
Authors	Harvey Huang, Nicholas M Gregg, Gabriela Ojeda Valencia, Benjamin H Brinkmann, Brian N Lundstrom, Gregory A Worrell, Kai J Miller, Dora Hermes
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004457.v1.0.1
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: 5
Recordings: 5
Tasks: 1

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS004457 class to access this dataset programmatically.

class eegdash.dataset.DS004457(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004457. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 6; recordings: 2801; 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/ds004457 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004457

Examples

>>> from eegdash.dataset import DS004457
>>> dataset = DS004457(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