A Quantitative Analysis of Multimodal Biomarkers in Alzheimer's Disease

2026-06-16

AI Summary

The interrelationships among multimodal biomarkers in Alzheimer’s disease remain poorly understood, leading to modeling challenges and redundant assessments. This study leverages the ADNI dataset to integrate tau-PET, structural MRI, cognitive scores, and APOE4 genotype, employing mutual information estimation, variance decomposition, and regional association analyses to systematically quantify redundancy and predictive dependencies across modalities. It provides the first characterization of the dynamic interaction between tau deposition and regional brain atrophy and disentangles structural from non-structural components underlying tau–cognition associations. The work identifies a core neurodegenerative pathway highly aligned with cognitive decline and proposes an information-theoretic strategy for selecting brain regions, offering a principled foundation for optimizing multimodal biomarker combinations in Alzheimer’s disease research and clinical assessment.

Abstract

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.

Problem

• Alzheimer's Disease
• multimodal biomarkers
• cross-modal relationships
• tau-PET
• structural MRI

Innovation

• multimodal biomarkers
• mutual information
• tau-PET
• structural atrophy
• neurodegenerative trajectory