CLDS: A novel centralized limited data sharing framework for multicenter fMRI-based brain diseases classification

Integrating multicenter resting-state functional magnetic resonance imaging (fMRI) datasets is essential for computer-aided diagnosis of brain diseases. However, differences in scanners and acquisition protocols lead to heterogeneity of data from different centers. To address this problem, we introduce a novel Centralized Limited Data Sharing (CLDS) framework for multicenter fMRI-based classification, which contains a centralized server and multiple local models, and propose three mechanisms to improve the classification performance. First, aiming at coordinating the global data without favoring any center, an adversarial network is introduced in the centralized server with limited noise-added data uploaded from each center. Second, a Gradient Adaptive Strategy based on the Tasks Association (GASTA) is proposed to restrict the gradient to a desirable direction. Third, a Correction Factor based on the Cosine Similarity (CFCS) is proposed to reduce the impact of non-independent and identically distributed (non-IID) of data on models. The experimental results show that CLDS exhibits superior classification performance to other related methods on the ADHD-200 dataset, with an accuracy of 70.4%. We also extend CLDS to the ABIDE-I dataset with an accuracy of 73.4%, demonstrating that CLDS has the potential for generalizability of other brain diseases and more centers.