Investigating the Amyloid-Tau-Neurodegeneration Framework in Alzheimer's Disease Using Semi-Supervised Multimodal Imaging Data Fusion.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is heterogeneous, complicating diagnosis and prognosis. Uncovering patterns that link abnormalities across amyloid-tau-neurodegeneration (A-T-N) framework may improve prediction of clinical diagnosis.

Methods

We applied SuperBigFLICA (SBF), a semi-supervised multimodal data fusion method, to maps of gray matter density, cortical thickness, pial surface area, amyloid PET, and tau PET in 274 ADNI-3 participants. The model was trained to derive 50 latent components most predictive of a continuous measure of cognitive decline. Latent components' subject loadings were subsequently used to predict diagnosis (cognitively normal, mild cognitive impairment, dementia) and APOE4 status using LASSO logistic regression, and were compared again

SUBMITTER: Cheng Y 

PROVIDER: S-EPMC12723765 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature