DS005530#

Depotentiation of emotional reactivity using TMR during REM sleep

Access recordings and metadata through EEGDash.

Citation: Viviana Greco, Tamas A. Foldes, Neil A. Harrison, Kevin Murphy, Marta Wawrzuta, Mahmoud E. A. Abdellahi, Penelope A. Lewis (2024). Depotentiation of emotional reactivity using TMR during REM sleep. 10.18112/openneuro.ds005530.v1.0.9

Modality: eeg Subjects: 18 Recordings: 431 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005530

dataset = DS005530(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 = DS005530(cache_dir="./data", subject="01")

Advanced query

dataset = DS005530(
    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{ds005530,
  title = {Depotentiation of emotional reactivity using TMR during REM sleep},
  author = {Viviana Greco and Tamas A. Foldes and Neil A. Harrison and Kevin Murphy and Marta Wawrzuta and Mahmoud E. A. Abdellahi and Penelope A. Lewis},
  doi = {10.18112/openneuro.ds005530.v1.0.9},
  url = {https://doi.org/10.18112/openneuro.ds005530.v1.0.9},
}

About This Dataset#

Disarming emotional memories using Targeted Memory Reactivation during Rapid Eye Movement sleep

This dataset contains fMRI and EEG data from a study investigating the effects of Targeted Memory Reactivation (TMR) during REM sleep on emotional reactivity. As well as behavioural data and ECG collected during behavioural tasks.

Study Design

View full README

Disarming emotional memories using Targeted Memory Reactivation during Rapid Eye Movement sleep

This dataset contains fMRI and EEG data from a study investigating the effects of Targeted Memory Reactivation (TMR) during REM sleep on emotional reactivity. As well as behavioural data and ECG collected during behavioural tasks.

Study Design

Participants rated the arousal of 48 affective images paired with semantically matching sounds. Heart rate deceleration was used as a measure of their autonomic arousal. Half of these sounds were cued during REM in the subsequent overnight sleep cycle. Participants rated the images in an MRI scanner with pulse oximetry 48 hours after encoding, and they completed an online follow up two weeks later.

Sessions

Baseline: Initial arousal ratings as well as overnight sleep with TMR
Session 48-H: fMRI scanning, pulse oximetry and arousal ratings (48 hours after baseline)
Session 2-Wk: Online follow-up (2 weeks after baseline)

Data Acquisition

fMRI: Acquired using a Siemens Magnetom Prisma 3T scanner with a 32-channel head coil
Heart Rate: Recorded using BrainVision BrainAmp ExG with ExG AUX box and multitrodes during the behavioural session and pulse oximetry during the fMRI session3
Polysomnography: Recorded using ten electrodes including 6 EEG channels (F3, F4, C3, C4, O1 and O2), 2 EMG channels and 2 EOG channels. All channels were live referenced to the average of left and right mastoids.

Dataset Contents

This initial upload contains: - T1-weighted structural images - Functional MRI data from Session 48-H - B0 field maps - Behavioural data from all sessions

Preprocessing

fMRI data were preprocessed using fMRIPrep 20.2.7. Details of the preprocessing pipeline can be found in the methods section of the associated publication.

T1-weighted structural scans were defaced using pydeface version 2.0.2 to ensure participant anonymity.

Within the behavioral data, the baseline ratings were centered within each participant. This was achieved by subtracting each participant’s mean baseline rating from the item-specific ratings they gave to the stimuli.

Additional Information

For more detailed information about the study design, methods, and results, please refer to the associated publication (citation to be added upon publication).

This dataset was initially converted to BIDS format using ezBIDS (https://brainlife.io/ezbids).

Contact

For questions about the MRI dataset, please contact: Dr Tamas Foldes foldesta@cardiff.ac.uk

Dataset Information#

Dataset ID	`DS005530`
Title	Depotentiation of emotional reactivity using TMR during REM sleep
Year	2024
Authors	Viviana Greco, Tamas A. Foldes, Neil A. Harrison, Kevin Murphy, Marta Wawrzuta, Mahmoud E. A. Abdellahi, Penelope A. Lewis
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005530.v1.0.9
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: 18
Recordings: 431
Tasks: 2

Channels & sampling rate

Channels: 10
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005530 class to access this dataset programmatically.

class eegdash.dataset.DS005530(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005530. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 17; recordings: 21; 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/ds005530 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005530

Examples

>>> from eegdash.dataset import DS005530
>>> dataset = DS005530(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