Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning.

ABSTRACT: Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T₁-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively no

SUBMITTER: Hwang G 

PROVIDER: S-EPMC9123890 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature