README.md

---
license: other
license_name: mass-general-brigham-non-commercial
license_link: LICENSE
tags:
  - brain
  - mri
  - neuroimaging
  - vit
  - foundation-model
  - medical-imaging
library_name: brainiac
pipeline_tag: feature-extraction
---

# BrainIAC — Brain Imaging Adaptive Core

**A generalizable foundation model for analysis of human brain MRI**

BrainIAC is a Vision Transformer (ViT-B/16) pretrained with SimCLR on structural brain MRI scans.
Published in [Nature Neuroscience](https://www.nature.com/articles/s41593-026-02202-6) (2026).

## Model Details

| Property | Value |
|----------|-------|
| Architecture | MONAI ViT-B/16³ (3D) |
| Parameters | 88.4M |
| Input | 96×96×96 single-channel brain MRI |
| Patches | 216 (6×6×6 grid, 16³ voxel patches) |
| Hidden dim | 768 |
| Layers | 12 transformer blocks |
| Heads | 12 attention heads |
| MLP dim | 3072 |
| Pretraining | SimCLR contrastive learning |
| Output | 768-dim feature vector (first patch token) |

## Files

- `backbone.safetensors` — Pretrained ViT backbone weights
- `config.json` — Model configuration
- `LICENSE` — Non-commercial academic research license

## Downstream Tasks

The backbone can be fine-tuned for:
- **Brain age prediction** (regression)
- **IDH mutation classification** (binary, dual-scan FLAIR+T1CE)
- **MCI classification** (binary)
- **Glioma overall survival** (binary, quad-scan T1+T1CE+T2+FLAIR)
- **MR sequence classification** (4-class: T1/T2/FLAIR/T1CE)
- **Time-to-stroke prediction** (regression)
- **Tumor segmentation** (UNETR decoder)

## Usage with brainiac (Rust)

```bash
cargo run --release --bin infer -- \
    --weights backbone.safetensors \
    --input brain_t1.nii.gz
```

```rust
use brainiac::{BrainiacEncoder, TaskType};

let (encoder, _) = BrainiacEncoder::<B>::load(
    "backbone.safetensors", None,
    TaskType::FeatureExtraction, 1, device,
)?;
let features = encoder.encode_nifti(Path::new("brain.nii.gz"))?;
// features: Vec<f32> with 768 dimensions
```

## Usage with Python

```python
import torch
from monai.networks.nets import ViT
from safetensors.torch import load_file

model = ViT(in_channels=1, img_size=(96,96,96), patch_size=(16,16,16),
            hidden_size=768, mlp_dim=3072, num_layers=12, num_heads=12)

weights = load_file("backbone.safetensors")
model.load_state_dict(weights, strict=False)
model.eval()

# features[0][:, 0] gives the 768-dim feature vector
features = model(preprocessed_mri)
```

## Preprocessing

Input MRI volumes must be:
1. Skull-stripped (HD-BET recommended)
2. Registered to standard space (MNI152)
3. Bias field corrected (N4)
4. Resized to 96×96×96 voxels (trilinear)
5. Z-score normalized (nonzero voxels only)

## Citation

```bibtex
@article{tak2026generalizable,
  title={A generalizable foundation model for analysis of human brain MRI},
  author={Tak, Divyanshu and Gormosa, B.A. and Zapaishchykova, A. and others},
  journal={Nature Neuroscience},
  year={2026},
  publisher={Springer Nature},
  doi={10.1038/s41593-026-02202-6}
}
```

## License

This model is licensed for **non-commercial academic research use only**.
Commercial use requires a separate license from Mass General Brigham.
See [LICENSE](LICENSE) for details.