Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 8; pp. 2391 - 2401
• Main Authors: Wang, Qianqian, Wang, Wei, Fang, Yuqi, Yap, Pew-Thian, Zhu, Hongtu, Li, Hong-Jun, Qiao, Lishan, Liu, Mingxia
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Autism; Biomarker; Biomarkers; Biomedical engineering; Brain; Brain - diagnostic imaging; Brain - physiology; brain disorder; Brain mapping; Brain Mapping - methods; Brain modeling; brain modularity; Cognition; Constraints; Deep learning; Female; Functional magnetic resonance imaging; functional MRI; Graph neural networks; Graphical representations; Humans; Image Processing, Computer-Assisted - methods; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Modular structures; Modularity; Modules; Network topologies; Network topology; Neural networks; Neural Networks, Computer; Neuroimaging; Representation learning; Topology
• DOI: 10.1109/TBME.2024.3370415
• ISSN: 0018-9294; 1558-2531

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.