Timely identification of Alzheimer's disease (AD) benefits from combining neuroimaging, fluid biomarkers, and cognitive assessments, yet in practice one or more modalities are often unavailable due to various factors such as cost, patient compliance, and procedural risks. Furthermore, conventional convolutional neural network (CNN) architectures and even Transformer-based models struggle to efficiently capture both local and global dependencies, especially when dealing with high-dimensional and highly heterogeneous medical data. In this study, we introduce a novel hierarchical information aggregation and dynamic fusion (HI-AD) framework for incomplete multimodal AD diagnosis. Our method couples a multi-view Hilbert curve-guided Mamba block with hierarchical spatial feature extraction to retain spatial continuity, model long-range dependencies, and integrate local context in neuroimaging data. To balance semantic alignment and modality-specific information, we propose a unified mutual information-driven learning objective with an active confidence evaluation mechanism, thereby preventing modality collapse and promoting robust representation learning. Extensive experiments on real-world datasets validate that our HI-AD framework consistently outperforms existing state-of-the-art methods across a diverse range of modality-missing scenarios, establishing an effective and generalizable solution for early-stage AD screening in heterogeneous clinical data environments.

Alzheimer's Disease (AD) poses a significant health threat to the aging population, underscoring the critical need for early diagnosis to delay disease progression and improve patient quality of life. Recent advances in heterogeneous multimodal artificial intelligence (AI) have facilitated comprehensive joint diagnosis, yet practical clinical scenarios frequently encounter incomplete modalities due to factors like high acquisition costs or radiation risks. Moreover, traditional convolution-based architecture face inherent limitations in capturing long-range dependencies and handling heterogeneous medical data efficiently. To address these challenges, in our proposed heterogeneous multimodal diagnostic framework (HAD), we develop a multi-view Hilbert curve-based Mamba block and a hierarchical spatial feature extraction module to simultaneously capture local spatial features and global dependencies, effectively alleviating spatial discontinuities introduced by voxel serialization. Furthermore, to balance semantic consistency and modal specificity, we build a unified mutual information learning objective in the heterogeneous multimodal embedding space, which maintains effective learning of modality-specific information to avoid modality collapse caused by model preference. Extensive experiments demonstrate that our HAD significantly outperforms state-of-the-art methods in various modality-missing scenarios, providing an efficient and reliable solution for early-stage AD diagnosis.