NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis

Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations. We propose $\mathit{\text{NeuroAlign}}$, a hierarchical framework for structured multimodal fusion. It introduces (1) $\mathit{\text{Dual-Modal}}\mathit{\text{Hierarchical}}\mathit{\text{Alignment}}$ (DMHA), which models multi-scale dynamic connectivity and aligns dynamic-static and functional-structural embeddings; and (2) $\mathit{\text{Dual-Domain}}\mathit{\text{Hierarchical}}\mathit{\text{Interaction}}$ (DDHI), which enables fine-grained modulation and global interaction between connectivity- and region-level features. To support feature-level inspection, we design $\mathit{\text{Synergistic}}\mathit{\text{Activation}}\mathit{\text{Mapping}}$ (SAM), a gradient-free, marker-oriented attribution method for DFC, SFC, ALFF, and FA. Evaluated on GUTCM, ADNI, and OASIS under five-fold validation, NeuroAlign achieves competitive MCI/SCD detection and preliminary cross-dataset transferability. Attribution analyses reveal modality-specific and partially consistent brain patterns, providing model-derived evidence for multimodal representation analysis.