Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks.

ABSTRACT: Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied mult

SUBMITTER: Avelar-Pereira B 

PROVIDER: S-EPMC10279806 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature