---
license: mit
language:
  - en
task_categories:
  - image-classification
tags:
  - medical
  - brain-data
  - mri
pretty_name: 3D Brain Structure MRI PCA
---

## 🧠 Model Summary
# brain2vec
An linear PCA model for brain structure T1 MRIs. The models takes in a 3d MRI NIfTI file and compresses to 1200 latent dimensions before reconstructing the image.


# Training data
[Radiata brain-structure](https://huggingface.co/datasets/radiata-ai/brain-structure): 3066 scans from 2085 individuals in the 'train' split. Mean age = 45.1 +- 24.5, including 2847 scans from cognitively normal subjects and 219 scans from individuals with an Alzheimer's disease clinical diagnosis.

# Example usage
```
# get brain2vec model repository
git clone https://huggingface.co/radiata-ai/brain2vec
cd brain2vec

# set up virtual environemt
python3 -m venv venv_brain2vec
source venv_brain2vec/bin/activate

# install Python libraries
pip install -r requirements.txt

# create the csv file inputs.csv listing the scan paths and other info
# this script loads the radiata-ai/brain-structure dataset
python create_csv.py

mkdir ae_cache
mkdir ae_output

# train the model
nohup python brain2vec.py train \
  --dataset_csv /home/ubuntu/brain2vec/inputs.csv \
  --cache_dir   ./ae_cache \
  --output_dir  ./ae_output \
  --n_epochs    10 \
> train_log.txt 2>&1 &

# run model inference to create *_embeddings.npz files
python brain2vec.py infererence \
  --dataset_csv home/ubuntu/brain2vec/inputs.csv \
  --aekl_ckpt /home/ubuntu/brain2vec/autoencoder_final.pth \
  --output_dir /home/ubuntu/brain2vec
```

# Methods
transform:
(80, 96, 80)
pixdim=2
10 epochs
    max_batch_size: int = 2,
    batch_size: int = 16,
    lr: float = 1e-4,

# References
Puglisi
Pinaya

# Citation
```
@dataset{Radiata-Brain-Structure,
  author    = {Jesse Brown and Clayton Young},
  title     = {Brain-Structure: Processed Structural MRI Brain Scans Across the Lifespan},
  year      = {2025},
  url          = {https://huggingface.co/datasets/radiata-ai/brain-structure},
  note      = {Version 1.0},
  publisher    = {Hugging Face}
}
```

# License
MIT License

Copyright (c) 2025

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in all
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