NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
NeuroAlign is a hierarchical multimodal fusion framework that integrates fMRI and DTI data for the analysis of Mild Cognitive Impairment (MCI). It introduces Dual-Modal Hierarchical Alignment (DMHA) and Dual-Domain Hierarchical Interaction (DDHI), along with a gradient-free attribution method called Synergistic Activation Mapping (SAM), achieving competitive detection performance on multiple datasets.
[2606.07635] NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
[Submitted on 31 May 2026]
Title:NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
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Abstract: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 \textit{NeuroAlign}, a hierarchical framework for structured multimodal fusion. It introduces (1) \textit{Dual-Modal Hierarchical Alignment} (DMHA), which models multi-scale dynamic connectivity and aligns dynamic-static and functional-structural embeddings; and (2) \textit{Dual-Domain Hierarchical Interaction} (DDHI), which enables fine-grained modulation and global interaction between connectivity- and region-level features. To support feature-level inspection, we design \textit{Synergistic Activation 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.
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Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07635 [cs.CV]
(or arXiv:2606.07635v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07635
arXiv-issued DOI via DataCite
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From: Shen Xiongri [view email] [v1] Sun, 31 May 2026 14:01:15 UTC (11,882 KB)
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