Generating brain activation maps from free-form text query

Gia H. Ngo, Minh Nguyen, Nancy F. Chen, Mert R. Sabuncu. Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021. arxiv

Text2Brain is a search engine for efficiently combing through rapidly growing wealth of neuroimaging literature brain activation patterns. It accepts not only keywords but also flexible free-form text queries. It encodes the text queries using a finetuned Transformer encoder (SciBERT) and generates whole-brain activation maps using a 3D convolutional neural network (CNN).

Demo is available at: braininterpreter.com

Activation maps predicted by Text2Brain for 3 synonymous queries: default network, self-generated thought, and task-unrelated thought. The ground-truth activation map is also included in the figure.

0. Install Anaconda
1. Clone this project from Github to some place on your computer (e.g. /home/gia/text2brain)
2. Create a Conda environment using the env.yml file

       conda env create -f env.yml -n text2brain

3. Download a checkpoint of the Text2Brain model from Google Drive
4. Extract the downloaded file. You should see a file named best_loss.pth.

       tar -xzvf text2brain_checkpoint.tar.gz

5. Move the file best_loss.pth into the project directory (e.g. /home/gia/text2brain)
6. Download the pre-trained uncased SciBERT model using this link
7. Extract the downloaded file.

       tar -xvf scibert_scivocab_uncased.tar

8. Move the scibert_scivocab_uncased folder into the project directory (e.g. /home/gia/text2brain)
9. Activate the Conda environment

       conda activate text2brain

10. To generate brain activation maps from free-form text query, run python predict.py <input_query> <output_file>. For example,

    python predict.py "self-generated thought" prediction.nii.gz

Please contact Gia at [email protected]