DS005688#

visStim

Access recordings and metadata through EEGDash.

Citation: Henry Tan, Devon Griggs, Lucas Chen, Kahte Culevski, Kat Floerchinger, Alissa Phutirat, Gabe Koh, Nels Schimek, Pierre Mourad (2024). visStim. 10.18112/openneuro.ds005688.v1.0.1

Modality: eeg Subjects: 20 Recordings: 360 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005688

dataset = DS005688(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 = DS005688(cache_dir="./data", subject="01")

Advanced query

dataset = DS005688(
    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{ds005688,
  title = {visStim},
  author = {Henry Tan and Devon Griggs and Lucas Chen and Kahte Culevski and Kat Floerchinger and Alissa Phutirat and Gabe Koh and Nels Schimek and Pierre Mourad},
  doi = {10.18112/openneuro.ds005688.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005688.v1.0.1},
}

About This Dataset#

Dataset description

This dataset was collected for the study “Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice” by Tan et al. (2024). The research demonstrates how transcranially delivered diagnostic ultrasound (tDUS) modulates the brain’s receptivity to external stimuli, using a blinking light stimulus in a mouse model. The study findings highlight the potential for diagnostic ultrasound to intentionally modulate brain function, paving the way for possible future clinical and therapeutic applications. Please cite the following paper when using this dataset: Tan H, Griggs DJ, Chen L, et al. Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice. Frontiers in Neuroscience. 2024. DOI: [in peer review].

License

View full README

Dataset description

This dataset was collected for the study “Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice” by Tan et al. (2024). The research demonstrates how transcranially delivered diagnostic ultrasound (tDUS) modulates the brain’s receptivity to external stimuli, using a blinking light stimulus in a mouse model. The study findings highlight the potential for diagnostic ultrasound to intentionally modulate brain function, paving the way for possible future clinical and therapeutic applications. Please cite the following paper when using this dataset: Tan H, Griggs DJ, Chen L, et al. Diagnostic ultrasound enhances, then reduces, exogenously induced brain activity of mice. Frontiers in Neuroscience. 2024. DOI: [in peer review].

License

This dataset is proprietary to the Department of Neurological Surgery, University of Washington, Seattle, WA, USA. Usage is restricted to academic and non-commercial research. Redistribution, modification, or commercial use is prohibited without prior permission. For inquiries, contact: Pierre D. Mourad (doumitt@uw.edu).

Acknowledgements

We thank the Department of Neurological Surgery, University of Washington, for internal funding. This research was supported by R01 NS119395 and P51 OD010425 (DJG) and the Mary Gates Research Scholarship** (HT).

Dataset Overview

This dataset comprises electrocorticography (ECoG) recordings from three cohorts of C57BL/6 mice exposed to combinations of diagnostic ultrasound (tDUS) and blinking light stimuli. The study investigates how tDUS influences the visual cortex’s response to external visual stimulation. Key Features: - Subjects: 20 C57BL/6 mice divided into three experimental cohorts:

Light-only cohort: Exposed to blinking light only.

US-only cohort: Exposed to tDUS without light.

US+Light cohort: Exposed to blinking light combined with tDUS.

Electrode Placement: ECoG electrodes targeted visual and somatosensory cortices.
Recording Conditions: Data recorded at 20 kS/s, filtered (5–55 Hz), and analyzed with MATLAB.

Data Structure

Data Files: - Raw ECoG Data: Continuous brain activity recordings. - Event-Triggered Data: Segment-specific RMS values for blinking light events. - Processed Data: Filtered and normalized brain activity traces.

Metadata: - Experimental conditions, cohort allocation, and baseline brain activity.

Methodology

Stimulation Protocols: Mice were exposed to blinking light stimuli (10 seconds per event) and tDUS delivered through a P21x5-1 scan head (Sonosite MicroMaxx system).
Data Collection: Baseline and event-triggered ECoG signals were recorded using LabChart software.
Analysis: RMS values normalized to baseline activity for comparative statistical analysis.

Outcomes Key Findings: 1. Enhanced Brain Activity: Simultaneous tDUS and blinking light increased cortical activity compared to light alone. 2. Persistent Effects: tDUS effects on brain activity persisted after stimulation ceased. 3. No Effect of tDUS Alone: tDUS without light did not activate cortical activity but reduced subsequent activity.

References For a complete list of references, please consult the manuscript.

Dataset Information#

Dataset ID	`DS005688`
Title	visStim
Year	2024
Authors	Henry Tan, Devon Griggs, Lucas Chen, Kahte Culevski, Kat Floerchinger, Alissa Phutirat, Gabe Koh, Nels Schimek, Pierre Mourad
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005688.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: 20
Recordings: 360
Tasks: 5

Channels & sampling rate

Channels: 4 (172), 1 (3)
Sampling rate (Hz): 10000.0 (148), 20000.0 (30)
Duration (hours): 0.0

API Reference#

Use the DS005688 class to access this dataset programmatically.

class eegdash.dataset.DS005688(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005688. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 20; recordings: 89; tasks: 5.

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/ds005688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005688

Examples

>>> from eegdash.dataset import DS005688
>>> dataset = DS005688(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