Joint Brain Structure-Function Analysis with Correlation-Consistent Learning for Alzheimer's Disease Diagnosis.

In neuroimaging-based Alzheimer's Disease (AD) diagnosis, effectively integrating structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data while preserving clinical interpretability remains a significant challenge. To address this issue, we propose a novel transformer-based framework that unifies heterogeneous imaging features into coherent region-level representations. Our approach uniquely leverages prior anatomical knowledge to guide attention toward AD-relevant regions while employing a learnable mapping mechanism that transforms sMRI spatial features into biologically meaningful regional representations. We implement a consistency constraint to ensure optimal alignment between structural and functional coupling across modalities, followed by a Bayesian fusion strategy to integrate these aligned multi-modal features. Through comprehensive evaluation on the ADNI dataset, our method demonstrates not only superior diagnostic accuracy compared to existing state-of-the-art approaches but also provides clinically interpretable insights into AD-related brain connectivity patterns. This work represents a significant advancement in multi-modal neuroimaging analysis for AD diagnosis, successfully combining enhanced diagnostic performance with clinical interpretability.