@inproceedings{
dong2024brainjepa,
title={Brain-{JEPA}: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking},
author={Zijian Dong and Li Ruilin and Yilei Wu and Thuan Tinh Nguyen and Joanna Su Xian Chong and Fang Ji and Nathanael Ren Jie Tong and Christopher Li Hsian Chen and Juan Helen Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gtU2eLSAmO}
}
We introduce *Brain-JEPA*, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction through fine-tuning. Furthermore, it excels in off-the-shelf evaluations (e.g., linear probing) and demonstrates superior generalizability across different ethnic groups, surpassing the previous large model for brain activity significantly. Brain-JEPA incorporates two innovative techniques: **Brain Gradient Positioning** and **Spatiotemporal Masking**. Brain Gradient Positioning introduces a functional coordinate system for brain functional parcellation, enhancing the positional encoding of different Regions of Interest (ROIs). Spatiotemporal Masking, tailored to the unique characteristics of fMRI data, addresses the challenge of heterogeneous time-series patches. These methodologies enhance model performance and advance our understanding of the neural circuits underlying cognition. Overall, Brain-JEPA is paving the way to address pivotal questions of building brain functional coordinate system and masking brain activity at the AI-neuroscience interface, and setting a potentially new paradigm in brain activity analysis through downstream adaptation.
Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking
Zijian Dong Li Ruilin Yilei Wu Thuan Tinh Nguyen Joanna Su Xian Chong Fang Ji Nathanael Ren Jie Tong Christopher Li Hsian Chen Juan Helen Zhou