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

Journal article

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.

Authors: Cheng, Y; Medina, A; Korponay, C; Beckmann, CF; Harper, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
• Volume: 18
• Issue: 2
• Article number: e70360
• Publication date: 2026-05-21
• Acceptance date: 2026-04-19
• DOI: 10.1002/dad2.70360
• EISSN: 2352-8729
• ISSN: 2352-8729
• Language: English
• Keywords: Alzheimer's disease; multimodal data fusion; amyloid–tau–neurodegeneration framework; multimodal neuroimaging; semi‐supervised learning; latent components