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Integrating the brain structural and functional connectivity features is ofgreat significance in both exploring brain science and analyzing cognitiveimpairment clinically. However, it remains a challenge to effectively fusestructural and functional features in exploring the brain network. In thispaper, a novel brain structure-function fusing-representation learning (BSFL)model is proposed to effectively learn fused representation from diffusiontensor imaging (DTI) and resting-state functional magnetic resonance imaging(fMRI) for mild cognitive impairment (MCI) analysis. Specifically, thedecomposition-fusion framework is developed to first decompose the featurespace into the union of the uniform and the unique spaces for each modality,and then adaptively fuse the decomposed features to learn MCI-relatedrepresentation. Moreover, a knowledge-aware transformer module is designed toautomatically capture local and global connectivity features throughout thebrain. Also, a uniform-unique contrastive loss is further devised to make thedecomposition more effective and enhance the complementarity of structural andfunctional features. The extensive experiments demonstrate that the proposedmodel achieves better performance than other competitive methods in predictingand analyzing MCI. More importantly, the proposed model could be a potentialtool for reconstructing unified brain networks and predicting abnormalconnections during the degenerative processes in MCI.

Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI