NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
arXiv:2606.07635v1 Announce Type: cross 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 Hier
The continuous advancements in AI and neuroimaging techniques are enabling more sophisticated analyses of complex biological data, pushing the boundaries of medical diagnostics.
Improved detection and analysis of conditions like cognitive impairment, such as MCI, can lead to earlier interventions and better patient outcomes, influencing healthcare and pharmaceutical sectors.
This research introduces a novel, hierarchical AI framework that can integrate diverse neuroimaging data more effectively, potentially enhancing the accuracy of MCI diagnosis and understanding.
- · Medical AI developers
- · Neurology patients
- · Healthcare providers
- · Academic researchers
- · Traditional diagnostic methods
More accurate and early diagnosis of neurodegenerative diseases becomes possible.
This could accelerate drug discovery and the development of targeted therapies for cognitive impairments.
Long-term societal costs associated with late-stage neurodegenerative care might decrease as early intervention becomes more prevalent.
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Read at arXiv cs.AI