Multimodal axes reveal individualized amyloid-β, tau, and neurodegeneration coupling in aging and Alzheimer’s disease
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Can we decode Alzheimer’s disease (AD) heterogeneity into a few portable axes that capture how amyloid-β, tau and neurodegeneration (A-T-N) spatially co vary in vivo? To answer this question, we built a pipeline that harmonizes longitudinal amyloid-β/tau PET and T1 MRI (gray matter) from ADNI cohort (12,430 images) with mixed effects modeling and then derived stage specific multimodal axes (mVCs) using linked component analysis, with robustness tested in simulations and external validation in the OASIS cohort (4,958 images). We identified a small set of multimodal axes that (i) recapitulate early tau weighted variation in cognitively unimpaired (CU) individuals, AD like A -T-N coupling in cognitively impaired (CI) individuals and atypical CU and CI participants with posterior (precuneus/occipitoparietal) and fronto insular/frontal weighted patterns, (ii) map onto domain specific cognition, APOE e4, and blood/CSF biomarkers of neurodegeneration, neuroaxonal injury and astrocyte activation, (iii) predict clinical transitions, (iv) generalize in an independent cohort, and (v) demonstrate modelling robustness to missing data, high dimensionality, and cross-cohort variability, enabling direct application of the extracted axes to new datasets for biomarker discovery and stratification. Multimodal axes provide a portable, interpretable layer for quantifying amyloid-β-tau-neurodegeneration coupling at the individual level, complementing current biomarker-based staging frameworks based on A-T-N status and tau PET topography, and can be computed on new datasets to aid clinical assessment and trial enrichment.
Significance Statement
We developed and validated a multimodal statistical pipeline to identify individualized patterns of association among core Alzheimer’s disease biomarkers: amyloid-β deposition, tau accumulation, and neurodegeneration. Applied to longitudinal PET and MRI data, the approach revealed distinct, reproducible axes of biomarker coupling across cognitively unimpaired and impaired individuals, linked to cognitive performance and clinical progression. By providing subject -level scores that quantify how pathologies co-express across brain regions, this framework supports fine-grained biomarker discovery, improves interpretation of Alzheimer’s disease heterogeneity, and can be extended to high -dimensional multimodal datasets in future biomarker studies
