TRIBE v2 is a deep multimodal brain encoding model that predicts fMRI brain responses to naturalistic stimuli (video, audio, text). It combines state-of-the-art text, audio and video models into a unified Transformer architecture that maps multimodal representations onto the cortical surface.
Quick start
Load a pretrained model from HuggingFace and predict brain responses to a video:
Predictions are for the "average" subject (see paper for details) and live on the fsaverage5 cortical mesh (~20k vertices).
They are offset by 5 seconds in the past, in order to compensate for the hemodynamic lag.
You can also pass text_path or audio_path to model.get_events_dataframe — text is automatically converted to speech and transcribed to obtain word-level timings.
For a full walkthrough with brain visualizations, see the Colab demo notebook.
Installation
Basic (inference only):
pip install -e .
With brain visualization:
pip install -e ".[plotting]"
With training dependencies (PyTorch Lightning, W&B, etc.):
pip install -e ".[training]"
Training a model from scratch
1. Set environment variables
Configure data/output paths and Slurm partition (or edit tribev2/grids/defaults.py directly):
tribev2/
├── main.py # Experiment pipeline: Data, TribeExperiment
├── model.py # FmriEncoder: Transformer-based multimodal→fMRI model
├── pl_module.py # PyTorch Lightning training module
├── demo_utils.py # TribeModel and helpers for inference from text/audio/video
├── eventstransforms.py # Custom event transforms (word extraction, chunking, …)
├── utils.py # Multi-study loading, splitting, subject weighting
├── utils_fmri.py # Surface projection (MNI / fsaverage) and ROI analysis
├── grids/
│ ├── defaults.py # Full default experiment configuration
│ └── test_run.py # Quick local test entry point
├── plotting/ # Brain visualization (PyVista & Nilearn backends)
└── studies/ # Dataset definitions (Algonauts2025, Lahner2024, …)
Contributing to open science
If you use this software, please share your results with the broader research community using the following citation:
bibtex
1@article{dAscoli2026TribeV2,
2 title={A foundation model of vision, audition, and language for in-silico neuroscience},
3 author={d'Ascoli, St{\'e}phane and Rapin, J{\'e}r{\'e}my and Benchetrit, Yohann and Brookes, Teon and Begany, Katelyn and Raugel, Jos{\'e}phine and Banville, Hubert and King, Jean-R{\'e}mi},
4 year={2026}
5}
License
This project is licensed under CC-BY-NC-4.0. See LICENSE for details.
