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Multimodal neuroimaging plays an indispensable role in the diagnosis, monitoring, and therapeutic evaluation of monoclonal antibody (mAb) therapies for Alzheimer's disease (AD). Structural MRI (sMRI) enables early detection of cerebral atrophy and amyloid-related imaging abnormalities (ARIA), a critical adverse effect associated with anti-Aβ immunotherapies. Positron emission tomography (PET) provides direct visualization and quantification of Aβ plaque clearance, serving as an objective biomarker of target engagement. Functional MRI (fMRI) has been investigated as a means to detect dynamic changes in brain network connectivity following treatment, though the evidence remains preliminary. The integration of these modalities significantly enhances diagnostic accuracy and allows for personalized assessment of treatment response. Furthermore, artificial intelligence (AI) technologies improve the efficiency and predictive power of imaging data analysis, supporting clinical decision-making. Despite these advances, challenges remain regarding the sensitivity and specificity of current imaging techniques, heterogeneity in treatment responses, and the need for long-term safety monitoring. Standardized imaging protocols, combined with multidisciplinary collaboration and robust AI-assisted modeling, are essential to optimize therapeutic outcomes and minimize risks in mAb-based AD treatment.
Wang et al. (2026) studied this question.