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Source code for "fMRI predictors based on language models of increasing complexity recover brain left lateralization"

by Laurent Bonnasse-Gahot and Christophe Pallier

Paper accepted at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

Dependencies

Python modules

See requirements.txt for the full list of packages used in this work. This file provides the exact version that was used, but the code is expected to work with other versions as well.

It is recommended to create a virtual environment to install the python modules, for example:

With Anaconda

conda create --name lpp python=3.10
conda activate lpp
pip install -r requirements.txt

Or with Pyenv

pyenv virtualenv 3.10 lpp
pyenv activate lpp
pip install -r requirements.txt

Once the environment is installed, you can launch jupyter notebook or jupyter lab to execute the python files (.py) or the notebooks (.ipynb) with the %run command.

fMRI data

Ref: Li, J., Bhattasali, S., Zhang, S., Franzluebbers, B., Luh, W., Spreng, R. N., Brennen, J., Yang, Y., Pallier, C., & Hale, J. (2022). Le Petit Prince multilingual naturalistic fMRI corpus. Scientific Data, 9, 530.

The fMRI data can be obtained at openneuro.org: doi:10.18112/openneuro.ds003643.v2.0.5.

In order to reproduce the main results of the paper (in English), you only need the annotation/EN and derivatives/sub-EN* subfolders.

Glove embeddings

Download the GloVe embeddings:

wget https://huggingface.co/stanfordnlp/glove/resolve/main/glove.6B.zip
unzip glove.6B.zip

Setting up paths and hugginface access_token

First, you must set all folders variable (home_folder and lpp_path) in llms_brain_lateralization.py.

In the same file, you should provide a valid acces_token in order to be able to access the various models in huggingface that require authentication.

Processing pipeline

The following list of commands describe the pipeline for processing the English data. For French and Chinese data, use the same pipeline with the --lang fr and --lang cn option respectively.

1. Resample fMRI data to 4x4x4mm voxels:

    python resample_fmri_data.py --lang en
   

2. Create a mask common to all subjects:

    python compute_mask.py --lang en
   

   to generate mask_lpp_en.nii.gz

3. Create the 7 roi masks used in the paper:

    python create_roi_masks.py
   

4. Compute the average subject

    python compute_average_subject_fmri.py --lang en
   

   This script also computes an evaluation of the inter-subjects reliable voxels and produces isc_10trials_en.gz which contains, for each voxel, an estimate of the inter-subjects correlation (isc), the correlation between an average subject made from half of the subjects and predicted values from held-out runs using another average subject made from the other half of the subjects as regressors (and this 10 times, using different group partitions).

5. Extract activations from LLMs.

    python extract_llm_activations.py --model XXX --lang en
   

   to get output of the neurons of each layer of model XXX; for instance, for gpt2:

    python extract_llm_activations.py --model gpt2 --lang en
   

   In order to extract from the all models, one can use the following bash lines:

    while read -r model_name; do
        python extract_llm_activations.py --model $model_name --lang en
    done < model_list_en
   

6. Fit the average fMRI subject using ridge regression. Run the script fit_average_subject.py. For instance, using the activations from gpt2 as extracted in the previous step, run python fit_average_subject.py --model gpt2 --lang en. In the paper, the whole model list described above is used, as follows (in bash):

    while read -r model_name; do
        python fit_average_subject.py --model $model_name --lang en
     done < model_list_en
   

7. Compute the baselines (random vectors, random embeddings and GloVe).

   For GloVe, first download the GloVe embeddings. Then run

    python extract_glove_activations.py
   

   to extract the embeddings, then

    python fit_average_subject.py --model glove
   

   to fit to the fMRI brain data.

   For the random baselines, use generate_random_activations.py. The paper uses the following bash code:

    for type in vector embedding
    do
        for d in 300 1024
        do
            for i in {1..10}
            do
                  python generate_random_activations.py --type $type --n_dims $d --seed $i --lang en;
                  python fit_average_subject.py --model random_${type}_${d}d_seed${i} --lang en;
            done
        done
    done
   

8. Analyze and visualize all the results, as described in the paper:

   %run analyze_results.ipynb

9. Additional analyses

• Impact of model training

  • Untrained models

    Use the same script as before to extract the activation, but using the --seed option with a value for the seed > 0. For instance:

    python extract_llm_activations.py --model gpt2 --seed 1 --lang en
    

    The activations are then saved with the "_untrained_seed1" suffix here. Following the example with gpt2, fitting this untrained model is then performed using the following command: python fit_average_subject.py --model gpt2_untrained_seed1 --lang en

  • Pythia

    for step in 0 1000 14000 36000 72000 143000
    do
    	python extract_llm_activations.py --model pythia-6.9b_step${step} --lang en
    	python fit_average_subject.py --model pythia-6.9b_step${step} --lang en
    done
    

  See analyze_results_training.ipynb for the analysis and visualization of the results.

• Other languages: Chinese and French

  Same pipeline as described above for English, but using the --lang cn option for Chinese and --lang fr for French. See analyze_results_cn_fr.ipynb for the analysis and visualization of the results with the Chinese or French data.

• Individual analyses

  In the paper, we analyze the first five English subjects, using the following bash script that uses the fit_individual_subject.py Python script:

   for sub_id in EN057 EN058 EN059 EN061 EN062
   do
     for model_name in gpt2 gpt2-medium gpt2-large gpt2-xl Qwen1.5-0.5B Qwen1.5-1.8B Qwen1.5-4B Qwen1.5-7B Qwen1.5-14B
     do
         python fit_individual_subject.py --model $model_name --subject $sub_id;
     done
   done
  

  See analyze_results_individuals.ipynb for the analysis and visualization of the results.

Preprint

arXiv:2405.17992

@article{lbg2024fmri,  
  title={fMRI predictors based on language models of increasing complexity recover brain left lateralization},  
  author={Bonnasse-Gahot, Laurent and Pallier, Christophe},  
  journal={arXiv preprint arXiv:2405.17992},  
  year={2024}
}