Add dataset card

README.md

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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 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"])         # 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
+
+# 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
+
+### 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