DCLNet: Double Collaborative Learning Network on Stationary-Dynamic Functional Brain Network for Brain Disease Classification

• Published in: IEEE transactions on image processing Vol. 34; pp. 4026 - 4039
• Main Authors: Zhou, Jie, Jie, Biao, Wang, Zhengdong, Zhang, Zhixiang, Bian, Weixin, Yang, Yang, Li, Hongwei, Sun, Fengyun, Liu, Mingxia
• Format: Journal Article
• Language: English
• Published: United States IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Brain; Brain - diagnostic imaging; Brain - physiopathology; brain disease; Brain diseases; Brain Diseases - classification; Brain Diseases - diagnostic imaging; Brain Diseases - physiopathology; Categories; Classification; Coders; Collaboration; collaborative encoder; Collaborative learning; Contrastive learning; Data mining; Depression; Diseases; Feature extraction; Federated learning; Female; Functional brain network; Functional magnetic resonance imaging; Humans; Image Interpretation, Computer-Assisted - methods; Learning; Machine Learning; Magnetic Resonance Imaging - methods; Male; Modules; Network analysis; Sun; Transformers
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2025.3579991

Stationary functional brain networks (sFBNs) and dynamic functional brain networks (dFBNs) derived from resting-state functional MRI characterize the complex interactions of the human brain from different aspects and could offer complementary information for brain disease analysis. Most current studies focus on sFBN or dFBN analysis, thus limiting the performance of brain network analysis. A few works have explored integrating sFBN and dFBN to identify brain diseases, and achieved better performance than conventional methods. However, these studies still ignore some valuable discriminative information, such as the distribution information of subjects between and within categories. This paper presents a Double Collaborative Learning Network (DCLNet), which takes advantage of both collaborative encoder and collaborative contrastive learning, to learn complementary information of sFBN and dFBN and distribution information of subjects between inter- and intra-categories for brain disease classification. Specifically, we first construct sFBN and dFBN using traditional correlation-based methods with rs-fMRI data, respectively. Then, we build a collaborative encoder to extract brain network features at different levels (i.e., connectivity-based, brain-region-based, and brain-network-based features), and design a prune-graft transformer module to embed the complementary information of the features at each level between two kinds of FBNs. We also develop a collaborative contrastive learning module to capture the distribution information of subjects between and within different categories, thereby learning the more discriminative features of brain networks. We evaluate the DCLNet on two real brain disease datasets with rs-fMRI data, with experimental results demonstrating the superiority of the proposed method.