DS006902#

Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy

Access recordings and metadata through EEGDash.

Citation: Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär (2025). Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy. 10.18112/openneuro.ds006902.v1.1.1

Modality: fnirs Subjects: 42 Recordings: 42 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006902

dataset = DS006902(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 = DS006902(cache_dir="./data", subject="01")

Advanced query

dataset = DS006902(
    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{ds006902,
  title = {Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy},
  author = {Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär},
  doi = {10.18112/openneuro.ds006902.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds006902.v1.1.1},
}

About This Dataset#

Regular physical activity is an important treatment constituent for chronic pain. To unravel the neuronal influence of exercise on pain, we investigated the neuronal changes during exercise-induced hypoalgesia in endurance athletes and controls. Twenty-two athletes (mean age: 33.3 ± 10.8 years) and twenty non-athletes (mean age: 28.9 ± 9.0 years) underwent High-Intensity Interval Training (HIIT) and pressure pain tests, while brain oxygenation was monitored using functional near-infrared spectroscopy to cover key regions of pain processing: the prefrontal cortex (PFC), sensory motor cortices, and posterior parietal cortex (PPC). During HIIT, both groups exhibited a steady increase in PFC oxyhemoglobin, with athletes showing a greater increase in the PPC area than non-athletes. As expected, athletes showed a significant reduction in pain perception after HIIT, whereas non-athletes did not. In line, athletes showed a significant decrease in oxyhemoglobin levels in all brain areas post-HIIT, while non-athletes only showed a decrease in sensory motor areas. Interestingly, in athletes, pain reduction correlated with the decrease in PFC oxyhemoglobin during painful stimulation, whereas no significant correlation was observed in non-athletes. The pronounced HIIT-induced increase in oxyhemoglobin in athletes may elevate baseline neural activity to a level where additional activation is limited, potentially reducing the salience of pain-related signals. This athlete-specific response may result from endurance training adaptations, such as enhanced microvascularization and oxygen delivery, promoting greater neural efficiency during high-intensity exercise. These findings highlight HIIT’s potential as a targeted pain management strategy for athletes and the need for tailored approaches in non-athletes. dataset: sub01-sub27 are athletes; sub29-sub53 are non-athletes

Dataset Information#

Dataset ID	`DS006902`
Title	Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy
Year	2025
Authors	Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006902.v1.1.1
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: 42
Recordings: 42
Tasks: 1

Channels & sampling rate

Channels: 112
Sampling rate (Hz): 7.627765064836003
Duration (hours): 0.0

API Reference#

Use the DS006902 class to access this dataset programmatically.

class eegdash.dataset.DS006902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006902. Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 43; recordings: 259; 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/ds006902 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006902

Examples

>>> from eegdash.dataset import DS006902
>>> dataset = DS006902(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