DS005065#

Heuristics in risky decision-making relate to preferential representation of information MEG data

Access recordings and metadata through EEGDash.

Citation: Evan M. Russek, Rani Moran, Yunzhe Liu, Ray Dolan, Quentin Huys (2024). Heuristics in risky decision-making relate to preferential representation of information MEG data. 10.18112/openneuro.ds005065.v1.0.0

Modality: meg Subjects: 21 Recordings: 3332 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005065

dataset = DS005065(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 = DS005065(cache_dir="./data", subject="01")

Advanced query

dataset = DS005065(
    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{ds005065,
  title = {Heuristics in risky decision-making relate to preferential representation of information MEG data},
  author = {Evan M. Russek and Rani Moran and Yunzhe Liu and Ray Dolan and Quentin Huys},
  doi = {10.18112/openneuro.ds005065.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005065.v1.0.0},
}

About This Dataset#

The task consisted of 13 scanner runs (except for subject 1 who completed 5 rather than 3 localizer runs). Runs 1-3 (1-5 for subject 1) are the localizer task. Runs 4-5 are non-analyzed data from the ‘probability learning’ task. Runs 6-13 (8-15 for subject 1) are the risky decision-making task.

Event times were recorded with a photodiode, which is accessible as a MEG channel. This has been processed so that event times are listed in derivatives/Event_Info_Tables. Raw times of events in the scan are in column “onset_time”. The corresponding index into the unprocessed MEG data is in column “scanner_onset_idx”. The onset into the downsampled data is in “onset_idx_ds”. In the table, each row corresponds to an event. Block number denotes which scanner run that event belongs to. For the localizer task (denoted in phase column), events are image onsets. “image_type” specifies the role of that image in the task (“CHOICE” or “OUTCOME”) and “image_number” denotes which choice or outcome it is (see paper Fig. 1). Finally, “image_name” denotes which image category was shown (e.g. “Hand”). For the task, events correspond to gamble information onset (Info), Probability stimulus presentation (“Choice”), response (“Gamble Response”) and outcome onset (“Outcome”). Columns denote which image was shown and what the response was (accept).

derivatives/Epoched_Data contains epoched preprocessed data for each subject for the localizer task and then around each choice in the main choice task. Both are epoched from from 0-500 ms following the event.

Code to analyze the data along with additional behavioral data is available at evanrussek/MEG_Heuristics_Risk_Preferential_Information

Dataset Information#

Dataset ID	`DS005065`
Title	Heuristics in risky decision-making relate to preferential representation of information MEG data
Year	2024
Authors	Evan M. Russek, Rani Moran, Yunzhe Liu, Ray Dolan, Quentin Huys
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005065.v1.0.0
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: 21
Recordings: 3332
Tasks: 1

Channels & sampling rate

Channels: 415 (210), 341 (65)
Sampling rate (Hz): 1200.0
Duration (hours): 0.0

API Reference#

Use the DS005065 class to access this dataset programmatically.

class eegdash.dataset.DS005065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005065. Modality: meg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 21; recordings: 275; 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/ds005065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005065

Examples

>>> from eegdash.dataset import DS005065
>>> dataset = DS005065(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