BrainLM model

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 and its code is available at this repository

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 at Yale University
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Model Sources [optional]

Repository: https://github.com/vandijklab/BrainLM
Paper: https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1
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Uses

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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

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Training Details

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Training Procedure

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Citation [optional]

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}
}

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