A graph transformer-based foundation model for brain functional connectivity network

• Published in: Pattern recognition Vol. 169; p. 111988
• Main Authors: Wang, Yulong, Calhoun, Vince D, Pearlson, Godfrey D, Kochunov, Peter, van Erp, Theo G.M., Du, Yuhui
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2026
• Subjects: Autoencoder; Brain network; Foundation model; Graph; Transformer; Graph; Transformer; Autoencoder; Brain network; Foundation model
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2025.111988

Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain function and widely used in neuroscience. We propose a foundation model tailored for brain functional connectivity networks (FCN). Our graph transformer model integrates node and edge embeddings to extract FCN features and adapts flexibly to classification, regression, and clustering via task-specific adapters. We validate the model on fMRI data from 10,718 scans across multiple tasks: gender classification, mental disorder classification (distinguishing schizophrenia or autism from healthy population), brain age prediction, and depressive and anxiety disorder biotyping. Compared to 14 competing methods, our model consistently outperforms them. Moreover, it facilitates biomarker discovery by identifying task-specific FC patterns. In summary, we present a novel, versatile foundation model for FCN that advances neuroimaging research through scalable and interpretable analysis.