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# BrainLM model

<!-- Provide a quick summary of what the model is/does. -->

The pretrained model of Brain Language Model (BrainLM) aims to achieve a general understanding of brain dynamics through self-supervised masked prediction. It is introduced in [this paper](https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1) and its code is available at [this repository](https://github.com/vandijklab/BrainLM)

## Model Details

### Model Description

We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the prediction of clinical variables and future brain states. In zero-shot inference, the model identifies functional networks and generates interpretable latent representations of neural activity. Furthermore, we introduce a novel prompting technique, allowing BrainLM to function as an in silico simulator of brain activity responses to perturbations. BrainLM offers a novel framework for the analysis and understanding of large-scale brain activity data, serving as a “lens” through which new data can be more effectively interpreted.



- **Developed by:** [van Dijk Lab](https://www.vandijklab.org/) at Yale University
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** https://github.com/vandijklab/BrainLM
- **Paper:** https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
 @article{ortega2023brainlm,
  title={BrainLM: A foundation model for brain activity recordings},
  author={Ortega Caro, Josue and Oliveira Fonseca, Antonio Henrique and Averill, Christopher and Rizvi, Syed A and Rosati, Matteo and Cross, James L and Mittal, Prateek and Zappala, Emanuele and Levine, Daniel and Dhodapkar, Rahul M and others},
  journal={bioRxiv},
  pages={2023--09},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}
```
    

**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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