  Foundation models pretrained on large-scale datasets via self-supervised
learning demonstrate exceptional versatility across various tasks. Due to the
heterogeneity and hard-to-collect medical data, this approach is especially
beneficial for medical image analysis and neuroscience research, as it
streamlines broad downstream tasks without the need for numerous costly
annotations. However, there has been limited investigation into brain network
foundation models, limiting their adaptability and generalizability for broad
neuroscience studies. In this study, we aim to bridge this gap. In particular,
(1) we curated a comprehensive dataset by collating images from 30 datasets,
which comprises 70,781 samples of 46,686 participants. Moreover, we introduce
pseudo-functional connectivity (pFC) to further generates millions of augmented
brain networks by randomly dropping certain timepoints of the BOLD signal. (2)
We propose the BrainMass framework for brain network self-supervised learning
via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling
(MRM) to bolster intra-network dependencies and regional specificity.
Furthermore, Latent Representation Alignment (LRA) module is utilized to
regularize augmented brain networks of the same participant with similar
topological properties to yield similar latent representations by aligning
their latent embeddings. Extensive experiments on eight internal tasks and
seven external brain disorder diagnosis tasks show BrainMass's superior
performance, highlighting its significant generalizability and adaptability.
Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities
and exhibits meaningful interpretation to various diseases, showcasing its
potential use for clinical applications.
