Investigating the amyloid–tau–neurodegeneration framework in Alzheimer's disease using semi‐supervised multimodal imaging data fusion

Abstract INTRODUCTION Alzheimer's disease (AD) heterogeneity complicates diagnosis and prognosis. Uncovering amyloid–tau–neurodegeneration (A–T–N) patterns may improve diagnostic prediction. METHODS We applied SuperBigFLICA (SBF), a semi‐supervised multimodal fusion method, to gray matter density, cortical thickness (CT), pial surface area, amyloid and tau positron emission tomography maps from 274 Alzheimer's Disease Neuroimaging Initiative 3 participants to derive 50 latent components predictive of cognitive decline. Subject loadings were then used to predict diagnosis (cognitively normal, mild cognitive impairment, dementia) and apolipoprotein E ( APOE ) ε4 status via least absolute shrinkage and selection operator logistic regression, compared to demographic, single‐modality, and naïve fusion comparator models. RESULTS SBF modestly predicted out‐of‐sample concurrent clinical severity (Clinical Dementia Rating Sum of Boxes; r = 0.21), yet models using SBF‐derived loadings were among the strongest comparator models (area under the receiver operating characteristic curve; = 0.80 for diagnosis; 0.83 for APOE ε4). Amyloid alterations in sensory areas best separated dementia, while a tri‐modal tau–neurodegeneration pattern related to disease progression. Loadings were validated through cerebrospinal fluid correlations. DISCUSSION SBF improves prediction and reveals interpretable patterns that better classify clinical diagnoses and APOE ε4 than traditional approaches.