DS006861#

Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals

Access recordings and metadata through EEGDash.

Citation: Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (2025). Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals. 10.18112/openneuro.ds006861.v1.0.2

Modality: eeg Subjects: 120 Recordings: 962 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006861

dataset = DS006861(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 = DS006861(cache_dir="./data", subject="01")

Advanced query

dataset = DS006861(
    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{ds006861,
  title = {Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals},
  author = {Szymon Mąka and Marta Chrustowicz and Łukasz Okruszek},
  doi = {10.18112/openneuro.ds006861.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds006861.v1.0.2},
}

About This Dataset#

Emotion Processing and Regulation Task (Static Stimuli) — tDCS‑EEG Dataset

This repository provides EEG recordings and behavioral data from the **Emotion Processing and Regulation**task conducted with **transcranial direct current stimulation (tDCS)**.

*Preregistration: https://osf.io/qdp3w_ *Preprint: https://osf.io/qtm8r_

View full README

Emotion Processing and Regulation Task (Static Stimuli) — tDCS‑EEG Dataset

This repository provides EEG recordings and behavioral data from the **Emotion Processing and Regulation**task conducted with **transcranial direct current stimulation (tDCS)**.

*Preregistration: https://osf.io/qdp3w_ *Preprint: https://osf.io/qtm8r_

Overview

Each participant took part in two experimental sessions:

**``ses-1``** — Sham stimulation
**``ses-2``** — Active stimulation

The order of sham/active conditions was counterbalanced across participants.

Participants

**N = 120** right‑handed, neurologically healthy adults with normal or corrected‑to‑normal vision.

*Missing data: Participant sub-005 completed only ses-1 due to a recording error during ses-2.

Experimental Task

Participants completed 120 trials per session, evenly allocated to a 2 (content: social, non-social) × 3 (regulation requirement: watch-neutral, watch-negative, reappraise-negative) factorial design. On each trial, they viewed a static image for 5 s and either watched or reappraised it as instructed. After each image, participants rated its arousal and then valence on separate 9-point scales.

tDCS Stimulation

System: Starstim 8 (Neuroelectrics, Spain) with NIC2 software.

Electrode Montage

Stimulation was targeted to the dorsolateral prefrontal cortex (dlPFC) in two alternative montages:

**Right dlPFC stimulation**

*Anode: **F4** *Returns: **FP2, FZ, FC2, FC6**
**Left dlPFC stimulation**

*Anode: **F3** *Returns: **FP1, FZ, FC1, FC5**

Electrode areas

*Anodal: 8 cm² *Return: π cm² *Ground: left earlobe

Stimulation Protocol

*Active: 2 mA for 20 min (with 30 s ramp‑up) *Sham: only ramp‑up periods at start and end; no sustained current * Questionnaires: After each session, participants completed the **tDCS Sensation Questionnaire**(Polish version: https://osf.io/ufszr_) to evaluate potential side effects. Additionally, after the final session, they indicated whether they believed each session involved*real*,*sham*, or*I don’t know* stimulation to assess **blinding effectiveness**.

EEG Acquisition

*Cap: 64‑channel QuickCap (32 EEG electrodes used) *Amplifier: Neuroscan SynampsRT *Sampling rate: 1000 Hz *Impedance: kept < 10 kΩ

Active EEG electrodes (32): FP1, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T7, C3, CZ, C4, T8, M1, TP7, CP3, CPZ, CP4, TP8, M2, P7, P3, PZ, P4, P8, O1, OZ, O2

Additional sensors

*EOG: Horizontal (HEO) and Vertical (VEO) channels were available on the cap but **were not connected** during recording. *Physio: ECG and GSR/EDA were recorded via auxiliary channels.

EEG Preprocessing

All preprocessing was performed in **MATLAB R2020b**using ** EEGLAB 2023.0**and **ERPLAB 9.10**. The full, commented pipeline is provided in code/Preprocessing_EEG.m.

Steps

Band‑pass filter: 0.1–30 Hz (zero‑phase Hamming‑windowed FIR)
Downsample**to **250 Hz
Re‑reference to average mastoids (M1, M2)
**Bad‑channel detection**using*clean_rawdata* (autocorrelation criterion = 0.8)
**ICA**with*runica*
Automatic IC rejection**using ** ADJUST**and ** SASICA
Spherical interpolation of removed channels
Epoching: −200 to 5000 ms relative to stimulus onset
Baseline correction: −200 ms pre‑stimulus
Artifact rejection: Step 1 – absolute amplitude on channels 1–30, epochs rejected if amplitude exceeded ±200 µV within −200 to 5000 ms. Step 2 – FASTER epoch_properties on channels 1–30, epochs rejected if any metric exceeded |z| > 2.
Condition‑wise averaging using ERPLAB

Derivatives & Ancillary Data

derivatives/processed_erps/

Averaged ERP files (.erp) for each participant and session after preprocessing.

derivatives/sideeffectsblinding_effectiveness/

side_effects_blinding_effectiveness_english.csv — _blinding effectiveness and side effects questionnaire
side_effects_blinding_effectiveness_data_dictionary.csv — data dictionary with variable names and value coding

code/

MATLAB preprocessing script and documentation: Preprocessing_EEG.m

Dataset Information#

Dataset ID	`DS006861`
Title	Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals
Year	2025
Authors	Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006861.v1.0.2
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: 120
Recordings: 962
Tasks: 1

Channels & sampling rate

Channels: 34 (239), 37 (239)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006861 class to access this dataset programmatically.

class eegdash.dataset.DS006861(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006861. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 120; recordings: 239; 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/ds006861 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006861

Examples

>>> from eegdash.dataset import DS006861
>>> dataset = DS006861(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