A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders

TL;DR

This paper introduces BrainGFM, a large-scale brain graph foundation model that uses graph contrastive learning and prompt-tuning to generalize across diverse brain atlases and disorders, enabling flexible neuroscience applications.

Contribution

It presents a novel graph-based pre-training paradigm for brain modeling, integrating multi-atlas data, prompts, and meta-learning for broad generalization and adaptability.

Findings

Pre-trained on 27 datasets with 25,000 subjects.

Achieved strong zero-shot and few-shot disorder classification.

Demonstrated effective cross-atlas and cross-disorder generalization.

Abstract

As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While most existing brain foundation models are pre-trained on time-series signals or connectome features, we propose a novel graph-based pre-training paradigm for constructing a brain graph foundation model. In this paper, we introduce the Brain Graph Foundation Model, termed BrainGFM, a unified framework that leverages graph contrastive learning and graph masked autoencoders for large-scale fMRI-based pre-training. BrainGFM is pre-trained on a diverse mixture of brain atlases with varying parcellations, significantly expanding the pre-training corpus and enhancing the model's ability to generalize across heterogeneous fMRI-derived brain representations. To support efficient and versatile downstream transfer, we integrate both graph prompts and language prompts into the model design, enabling BrainGFM to flexibly adapt to a wide range of atlases, neurological and psychiatric disorders, and task settings. Furthermore, we employ meta-learning to optimize the graph prompts, facilitating strong generalization to previously unseen disorders under both few-shot and zero-shot learning conditions via language-guided prompting. BrainGFM is pre-trained on 27 neuroimaging datasets spanning 25 common neurological and psychiatric disorders, encompassing 2 types of brain atlases (functional and anatomical) across 8 widely-used parcellations, and covering over 25,000 subjects, 60,000 fMRI scans, and a total of 400,000 graph samples aggregated across all atlases and parcellations.