Specificity-Aware Federated Graph Learning for Brain Disorder Analysis with Functional MRI

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive solution to explore abnormal brain connectivity patterns caused by brain disorders. Graph neural network (GNN) has been widely used for fMRI representation learning and brain disorder analysis, thanks to its potent graph representation abilities. Training a generalizable GNN model often requires large-scale subjects from different medical centers/sites, but the traditional centralized utilization of multi-site data unavoidably encounters challenges related to data privacy and storage. Federated learning (FL) can coordinate multiple sites to train a shared model without centrally integrating multi-site fMRI data. However, previous FL-based methods for fMRI analysis usually ignore specificity of each site, including factors such as age, gender, and population. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for fMRI-based brain disorder diagnosis. The proposed SFGL consists of a shared branch and a personalized branch, where the parameters of the shared branch are sent to a server and the parameters of the personalized branch remain in each local site. In the shared branch, we employ a graph isomorphism network and a Transformer to learn dynamic representations from fMRI data. In the personalized branch, vectorized representations of demographic information (i.e., gender, age, and education) and functional connectivity network are integrated to capture specificity of each site. We aggregate representations learned by shared branches and personalized branches for classification. Experimental results on two fMRI datasets with a total of 1, 218 subjects demonstrate that SFGL outperforms several state-of-the-art methods.