Multi-Modal Brain Network Fusion for Intelligent Diagnostic Devices

Embedding multi-modal brain network analysis technology into consumer electronics, such as smart wearables, helps enable early intelligent diagnosis of brain diseases. Recent studies confirm that the functional-structural coupling in certain regions is more tightly correlated than in others. However, existing multi-modal methods often directly fuse functional brain networks (FBN) and structural brain networks (SBN), ignoring the regional heterogeneity between them. Additionally, identity information encoded in brain networks may interfere with disease diagnosis. In this paper, we develop a multi-modal brain network fusion method with regional heterogeneity constraints, and design a feature decoupling module to alleviate disease-irrelevant information. Specifically, we first divide FBN and SBN into multiple subnetworks, and introduce penalty weights to reduce the communication cost between cross-modal brain regions within the subnetwork while increasing the cost between different subnetworks. Then, under the regional heterogeneity constraints, we adopt optimal transport to simulate the transfer of brain region hubness from FBN to SBN, thereby effectively integrating the complex cross-modal interactions. Furthermore, we design a feature decoupling module to suppress ineffective features and enhance the discrimination between modality-specific features and multi-modal features. Experimental results show that the proposed method has promising performance and can identify multi-modal biomarkers for brain disease diagnosis.