Brain Dynamics Foundation Model: Pretraining and Adaption for Disease Prognosis and Trait Prediction

Foundation models have emerged as powerful tools for analyzing large-scale brain activity data. In this talk, I will present recent advances in brain foundation models, focusing on our work on Brain-JEPA, which introduces a Joint-Embedding Predictive Architecture (JEPA) for brain dynamics. Brain-JEPA achieves outstanding performance across multiple tasks including demographic prediction, disease diagnosis, and trait prediction. The model features two key innovations: Brain Gradient Positioning, which introduces a functional coordinate system for brain parcellation, and Spatiotemporal Masking, which addresses the unique challenges of heterogeneous fMRI time-series patches. I will also briefly discuss our complementary approaches including Scaffold Prompt Tuning (ScaPT) for efficient adaptation of brain foundation model and Brain Tokenized Graph Transformer (TokenGT) for longitudinal analysis. These advances demonstrate the potential of foundation models to transform our understanding and analysis of brain activity data.