Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer's disease progression.

ABSTRACT:

Introduction

Genome-wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity.

Methods

We use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer's Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression.

Results

CNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate

SUBMITTER: Li Y 

PROVIDER: S-EPMC8120261 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature