Transfer learning-trained convolutional neural networks identify novelmri biomarkers of alzheimer’s disease progression

Abstract

Introduction: Genome-wide association studies (GWAS) for late onset Alzheimer’s disease (AD) maymiss genetic variants relevant for delineating disease stageswhen 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 theAlzheimer’s DiseaseNeuroimaging 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 genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, APP-regulated inflammation response, and insulin resistance. Discussion: This is the first attempt to show that non-invasive MRI biomarkers are linked toADprogression characteristics, reinforcing their use in earlyADdiagnosis and monitoring.