Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research -- aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization -- the relationships among these modalities remain poorly understood. A systematic analysis of their dynamic interaction is essential for improving disease modeling, identifying redundant assessments, and reducing patient burden and acquisition costs. In this paper, we present a quantitative analysis of multimodal AD biomarkers by integrating tau-PET, structural MRI, cognitive scores (MMSE and CDR), and APOE4 data from 789 subjects drawn from the ADNI dataset. In our analyses, we (A) quantify cross-modal mutual information and explained variance to assess redundancy and predictive dependencies; (B) examine associations between tau topologies and structural atrophy across brain regions to select informative ROIs; (C) perform a statistical decomposition of the tau-cognition association into atrophy-related and atrophy-independent components; (D) and identify a dominant neurodegenerative trajectory that aligns with cognitive decline. This study provides a systematic characterization of cross-modal relationships, improving the interpretability and selection of biomarkers in AD. Code is publicly available at: https://github.com/antonioscardace/Multimodal-AD.

翻译：尽管阿尔茨海默病（AD）研究中越来越多地采用多模态方法——旨在整合分子、结构、临床和遗传生物标志物以增强疾病表征——但这些模态之间的相互关系仍知之甚少。对其动态交互进行系统性分析对于改进疾病建模、识别冗余评估以及减轻患者负担和采集成本至关重要。本文通过整合来自ADNI数据集的789名受试者的tau-PET、结构MRI、认知评分（MMSE和CDR）及APOE4数据，对AD多模态生物标志物进行了定量分析。在分析中，我们（A）量化了跨模态互信息和解释方差以评估冗余性和预测依赖性；（B）检验了脑区tau拓扑结构与结构性萎缩之间的关联以选择信息丰富的ROI；（C）对tau-认知关联进行统计分解，将其分为萎缩相关和萎缩独立两部分；（D）确定了与认知衰退相符的主导性神经退行轨迹。本研究系统性地阐明了跨模态关系，提高了AD生物标志物的可解释性和选择效率。代码公开获取地址：https://github.com/antonioscardace/Multimodal-AD。