Datasets:

hugging-science
/

arc-aphasia-bids

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----
-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 | 441 sessions* |
-| T2-weighted scans | 439 sessions* |
-| FLAIR scans | 231 sessions* |
-| 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 exactly one scan. Sessions with multiple runs of the same structural modality are set to `None` to avoid ambiguity (3 T1w, 1 T2w, 2 FLAIR sessions affected).
-- **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
-    "t2w": <nibabel.Nifti1Image>,              # T2-weighted structural
-    "t2w_acquisition": "space_2x",             # T2w sequence type
-    "flair": <nibabel.Nifti1Image>,            # FLAIR structural
-    "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` | Nifti | T1-weighted structural MRI (nullable) |
-| `t2w` | Nifti | T2-weighted structural MRI (nullable) |
-| `t2w_acquisition` | string | T2w acquisition type: `space_2x`, `space_no_accel`, `turbo_spin_echo` (nullable) |
-| `flair` | Nifti | FLAIR structural MRI (nullable) |
-| `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 was collected at the University of South Carolina and Medical University of South Carolina as part of ongoing aphasia recovery research. All participants provided informed consent under IRB-approved protocols.
-### Annotations
-Lesion masks were manually traced by trained neuroimaging experts on T1-weighted or FLAIR images, following established stroke lesion delineation protocols.
-## Personal and Sensitive Information
-- **De-identified:** All data has been de-identified per HIPAA guidelines
-- **Defaced:** Structural MRI images have been defaced to prevent facial reconstruction
-- **No PHI:** No protected health information is included
-- **Consent:** All participants consented to public data sharing
-## 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
-### Known Biases
-- **Geographic:** Data collected primarily from Southeastern US medical centers
-- **Age:** Stroke predominantly affects older adults; pediatric cases underrepresented
-- **Severity:** Very severe aphasia cases may be underrepresented due to consent requirements
-### 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 (1-12 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"])         # 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 x["t2w"] is not None
-        and x["t2w_acquisition"] in ("space_2x", "space_no_accel")
-    )
-)
-# Returns 222 SPACE samples (115 space_2x + 107 space_no_accel)
-# Clinical metadata analysis
-import pandas as pd
-df = ds.to_pandas()[[
-    "subject_id", "session_id", "age_at_stroke",
-    "sex", "race", "wab_aq", "wab_days", "wab_type"
-]]
-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
-### 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

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