Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases

Abstract

Brain network analysis provides an interpretable framework for characterizing brain organization and has been widely used for neurological disorder identification. Recent advances in self-supervised learning have motivated the development of brain network foundation models. However, existing approaches are often limited by atlas dependency, insufficient exploitation of multiple network views, and weak incorporation of anatomical priors. In this work, we propose MV-BrainFM, a multi-view brain network foundation model designed to learn generalizable and scalable representations from brain networks constructed with arbitrary atlases. MV-BrainFM explicitly incorporates anatomical distance information into Transformer-based modeling to guide inter-regional interactions, and introduces an unsupervised cross-view consistency learning strategy to align representations from multiple atlases of the same subject in a shared latent space. By jointly enforcing within-view robustness and cross-view alignment during pretraining, the model effectively captures complementary information across heterogeneous network views while remaining atlas-aware. In addition, MV-BrainFM adopts a unified multi-view pretraining paradigm that enables simultaneous learning from multiple datasets and atlases, significantly improving computational efficiency compared to conventional sequential training strategies. The proposed framework also demonstrates strong scalability, consistently benefiting from increasing data diversity while maintaining stable performance across unseen atlas configurations. Extensive experiments on more than 20K subjects from 17 fMRI datasets show that MV-BrainFM consistently outperforms 14 existing brain network foundation models and task-specific baselines under both single-atlas and multi-atlas settings.

Figures (8)

• Figure 1: Comparison of representative brain foundation models.
• Figure 2: The architecture of our proposed MV-BrainFM using Schaefer100 and AAL116 as example. (a) Multiple brain network datasets with various atlases are utilized for pretraining. (b) Datasets with arbitrary atlases can be introduced to finetune the model for downstream tasks with a simple prediction head. (c) The proposed Distance-Biased Transformer (DBT) Encoder.
• Figure 3: Results on all 4 atlases. Note that BASC122 is the external atlas, which the models have never seen during pretraining.
• Figure 4: Scaling behavior with increasing pretraining atlases. We progressively increase the number of atlases used during pretraining and evaluate the downstream performance on 6 tasks using the Schaefer100 atlas. The results show that MV-BrainFM consistently benefits from larger and more diverse pretraining data, demonstrating clearer scaling behavior than BrainGFM.
• Figure 5: AUC performance of MV-BrainFM using the Schaefer100 atlas under different hidden dimensions (0.95M–31.35M parameters). Increasing model capacity improves performance from very small models, but larger models do not consistently yield additional gains, indicating that data scale may be a more critical factor than parameter scale.