---
license: cc0-1.0
task_categories:
  - image-segmentation
  - image-classification
language:
  - en
tags:
  - medical
  - neuroimaging
  - mri
  - brain
  - stroke
  - aphasia
  - BIDS
  - diffusion
  - fMRI
size_categories:
  - n<1K
---

# Aphasia Recovery Cohort (ARC)

Multimodal neuroimaging dataset for stroke-induced aphasia research.

## Dataset Summary

The Aphasia Recovery Cohort (ARC) is a large-scale, longitudinal neuroimaging dataset containing multimodal MRI scans from **230 chronic stroke patients** with aphasia. This HuggingFace-hosted version provides direct Python access to the BIDS-formatted data with embedded NIfTI files.

| Metric | Count |
|--------|-------|
| Subjects | 230 |
| Sessions | 902 |
| T1-weighted scans | 444 sessions (447 runs)* |
| T2-weighted scans | 440 sessions (441 runs)* |
| FLAIR scans | 233 sessions (235 runs)* |
| BOLD fMRI (naming40 task) | 750 sessions (894 runs) |
| BOLD fMRI (resting state) | 498 sessions (508 runs) |
| Diffusion (DWI) | 613 sessions (2,089 runs) |
| Single-band reference | 88 sessions (322 runs) |
| Expert lesion masks | 228 |

*Sessions with multiple runs of the same structural modality now include all runs as a list.

- **Source:** [OpenNeuro ds004884](https://openneuro.org/datasets/ds004884)
- **Paper:** [Gibson et al., Scientific Data 2024](https://doi.org/10.1038/s41597-024-03819-7)
- **License:** CC0 1.0 (Public Domain)

## Supported Tasks

- **Lesion Segmentation:** Expert-drawn lesion masks enable training/evaluation of stroke lesion segmentation models
- **Aphasia Severity Prediction:** WAB-AQ scores (0-100) provide continuous severity labels for regression tasks
- **Aphasia Type Classification:** WAB-derived aphasia type labels (Broca's, Wernicke's, Anomic, etc.)
- **Longitudinal Analysis:** Multiple sessions per subject enable recovery trajectory modeling
- **Diffusion Analysis:** Full bval/bvec gradients enable tractography and diffusion modeling
- **Task-based fMRI:** Naming40 and resting-state runs separated for targeted analysis

## Languages

Clinical metadata and documentation are in English.

## Dataset Structure

### Data Instance

Each row represents a single scanning session (subject + timepoint):

```python
{
    "subject_id": "sub-M2001",
    "session_id": "ses-1",
    "t1w": [<nibabel.Nifti1Image>, ...],       # T1-weighted structural (list of runs)
    "t2w": [<nibabel.Nifti1Image>, ...],       # T2-weighted structural (list of runs)
    "t2w_acquisition": "space_2x",             # T2w sequence type
    "flair": [<nibabel.Nifti1Image>, ...],     # FLAIR structural (list of runs)
    "bold_naming40": [<Nifti1Image>, ...],     # Naming task fMRI runs
    "bold_rest": [<Nifti1Image>, ...],         # Resting state fMRI runs
    "dwi": [<Nifti1Image>, ...],               # Diffusion runs
    "dwi_bvals": ["0 1000 1000...", ...],      # b-values per run
    "dwi_bvecs": ["0 0 0\n1 0 0\n...", ...],   # b-vectors per run
    "sbref": [<Nifti1Image>, ...],             # Single-band references
    "lesion": <nibabel.Nifti1Image>,           # Expert lesion mask
    "age_at_stroke": 58.0,
    "sex": "M",
    "race": "w",
    "wab_aq": 72.5,
    "wab_days": 180.0,
    "wab_type": "Anomic"
}
```

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `subject_id` | string | BIDS subject identifier (e.g., "sub-M2001") |
| `session_id` | string | BIDS session identifier (e.g., "ses-1") |
| `t1w` | Sequence[Nifti] | T1-weighted structural MRI runs |
| `t2w` | Sequence[Nifti] | T2-weighted structural MRI runs |
| `t2w_acquisition` | string | T2w acquisition type: `space_2x`, `space_no_accel`, `turbo_spin_echo` (nullable) |
| `flair` | Sequence[Nifti] | FLAIR structural MRI runs |
| `bold_naming40` | Sequence[Nifti] | BOLD fMRI runs for naming40 task |
| `bold_rest` | Sequence[Nifti] | BOLD fMRI runs for resting state |
| `dwi` | Sequence[Nifti] | Diffusion-weighted imaging runs |
| `dwi_bvals` | Sequence[string] | b-values for each DWI run (space-separated) |
| `dwi_bvecs` | Sequence[string] | b-vectors for each DWI run (3 lines, space-separated) |
| `sbref` | Sequence[Nifti] | Single-band reference images |
| `lesion` | Nifti | Expert-drawn lesion segmentation mask (nullable) |
| `age_at_stroke` | float32 | Subject age at stroke onset in years |
| `sex` | string | Biological sex ("M" or "F") |
| `race` | string | Self-reported race: "b" (Black), "w" (White), or null |
| `wab_aq` | float32 | Western Aphasia Battery Aphasia Quotient (0-100) |
| `wab_days` | float32 | Days since stroke when WAB was administered |
| `wab_type` | string | Aphasia type classification |

### Data Splits

| Split | Sessions | Description |
|-------|----------|-------------|
| train | 902 | All sessions (no predefined train/test split) |

Note: Users should implement their own train/validation/test splits, ensuring no subject overlap between splits for valid evaluation.

## Dataset Creation

### Curation Rationale

The ARC dataset was created to address the lack of large-scale, publicly available neuroimaging data for aphasia research. It enables:

- Development of automated lesion segmentation algorithms
- Machine learning models for aphasia severity prediction
- Studies of brain plasticity and language recovery

### Source Data

Data were acquired under studies approved by the Institutional Review Board at the University of South Carolina (per the OpenNeuro ds004884 `dataset_description.json`).

### Annotations

Expert-drawn lesion segmentation masks are provided in `derivatives/lesion_masks/`.

## Personal and Sensitive Information

- **Anonymized:** OpenNeuro ds004884 `dataset_description.json` states the final dataset is fully anonymised.

## Considerations for Using the Data

### Social Impact

This dataset enables research into:

- Improved stroke rehabilitation through better outcome prediction
- Automated clinical tools for aphasia assessment
- Understanding of brain-language relationships

### Potential Biases

- **Site:** Data were acquired under University of South Carolina IRB approval (per OpenNeuro metadata)
- **Age:** Adult cohort (`age_at_stroke` ranges from 27 to 80 years in participants.tsv)

### Known Limitations

- Not all sessions have all modalities (check for None/empty lists)
- Lesion masks available for 228/230 subjects
- Longitudinal follow-up varies by subject (1-30 sessions)

## Usage

```python
from datasets import load_dataset

ds = load_dataset("hugging-science/arc-aphasia-bids", split="train")

# Access a session
session = ds[0]
print(session["subject_id"])  # "sub-M2001"
print(session["t1w"][0])      # nibabel.Nifti1Image
print(session["wab_aq"])      # Aphasia severity score

# Access BOLD by task type
for run in session["bold_naming40"]:
    print(f"Naming40 run shape: {run.shape}")

for run in session["bold_rest"]:
    print(f"Resting state run shape: {run.shape}")

# Access DWI with gradient information
for i, (dwi_run, bval, bvec) in enumerate(zip(
    session["dwi"], session["dwi_bvals"], session["dwi_bvecs"]
)):
    print(f"DWI run {i+1}: shape={dwi_run.shape}")
    print(f"  b-values: {bval[:50]}...")
    print(f"  b-vectors: {bvec[:50]}...")

# Filter by T2w acquisition type (for paper replication)
space_only = ds.filter(
    lambda x: (
        x["lesion"] is not None
        and len(x["t2w"]) > 0
        and x["t2w_acquisition"] in ("space_2x", "space_no_accel")
    )
)
# Returns 223 SPACE samples (115 space_2x + 108 space_no_accel)

# Clinical metadata analysis
import pandas as pd

# Select only scalar columns to avoid loading NIfTI columns into RAM
df = ds.select_columns([
    "subject_id", "session_id", "age_at_stroke",
    "sex", "race", "wab_aq", "wab_days", "wab_type"
]).to_pandas()
print(df.describe())
```

## Technical Notes

### Multi-Run Modalities

Functional and diffusion modalities support multiple runs per session:

- Empty list `[]` = no data for this session
- List with items = all runs for this session, sorted by filename

### DWI Gradient Files

Each DWI run has aligned gradient information:

- `dwi_bvals`: Space-separated b-values (e.g., "0 1000 1000 1000...")
- `dwi_bvecs`: Three lines of space-separated vectors (x, y, z directions)

These are essential for diffusion tensor imaging (DTI) and tractography analysis.

### Memory Considerations

NIfTI files are loaded on-demand. For large-scale processing:

```python
for session in ds:
    process(session)
    # Data is garbage collected after each iteration
```

### Original BIDS Source

This dataset is derived from [OpenNeuro ds004884](https://openneuro.org/datasets/ds004884). The original BIDS structure is preserved in the column naming and organization.

## Additional Information

### Dataset Curators

- **Original Dataset:** Gibson et al. (University of South Carolina)
- **HuggingFace Conversion:** The-Obstacle-Is-The-Way

### Licensing

This dataset is released under **CC0 1.0 Universal (Public Domain)**. You can copy, modify, distribute, and perform the work, even for commercial purposes, all without asking permission.

## Citation

```bibtex
@article{gibson2024arc,
  title={The Aphasia Recovery Cohort, an open-source chronic stroke repository},
  author={Gibson, Makayla and Newman-Norlund, Roger and Bonilha, Leonardo and Fridriksson, Julius and Hickok, Gregory and Hillis, Argye E and den Ouden, Dirk-Bart and Rorden, Christopher},
  journal={Scientific Data},
  volume={11},
  pages={981},
  year={2024},
  publisher={Nature Publishing Group},
  doi={10.1038/s41597-024-03819-7}
}
```

## Contributions

Thanks to [@The-Obstacle-Is-The-Way](https://github.com/The-Obstacle-Is-The-Way) for converting this dataset to HuggingFace format with native `Nifti()` feature support.

## Changelog

### v4 (December 2025)

- **BREAKING:** `t1w`, `t2w`, `flair` changed from `Nifti()` to `Sequence(Nifti())` for full data fidelity
- **FIX:** 6 sessions with multiple structural runs now include all files (previously set to `None`)
- **NOTE:** Most sessions have exactly 1 structural scan; access via `session["t2w"][0]`

### v3 (December 2025)

- **RETRACTED:** Attempted fix for 222 → 223 SPACE samples was incorrect diagnosis
- **NOTE:** The missing sample is caused by a schema design flaw (see v4 fix above), not upload issues

### v2 (December 2025)

- **BREAKING:** `bold` column split into `bold_naming40` and `bold_rest` for task-specific analysis
- **NEW:** `dwi_bvals` and `dwi_bvecs` columns for diffusion gradient information
- **NEW:** `race` column from participants.tsv
- **NEW:** `wab_days` column (days since stroke when WAB administered)
- **NEW:** `t2w_acquisition` column for T2w sequence type filtering

### v1 (December 2025)

- Initial release with 13 columns