Wang Yulong, Calhoun Vince D, Pearlson Godfrey D, Kochunov Peter, van Erp Theo G M, Du Yuhui
School of Computer and Information Technology, Shanxi University, Taiyuan, China.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
Pattern Recognit. 2026 Jan;169. doi: 10.1016/j.patcog.2025.111988. Epub 2025 Jun 10.
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 subjects 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.
尽管基础模型在许多医学成像领域取得了进展，但它们在神经图像分析中的缺失限制了神经科学和临床实践的发展。脑功能连接（FC）分析是理解脑功能的核心，并且在神经科学中广泛应用。我们提出了一种专门针对脑功能连接网络（FCN）的基础模型。我们的图变压器模型整合了节点和边嵌入以提取FCN特征，并通过特定任务的适配器灵活地适应分类、回归和聚类。我们在来自10718名受试者的多个任务的功能磁共振成像（fMRI）数据上验证了该模型：性别分类、精神障碍分类（区分精神分裂症或自闭症与健康人群）、脑年龄预测以及抑郁和焦虑症生物分型。与14种竞争方法相比，我们的模型始终优于它们。此外，它通过识别特定任务的FC模式促进了生物标志物的发现。总之，我们提出了一种新颖、通用的FCN基础模型，通过可扩展和可解释的分析推动了神经成像研究。
