Dynamic Changes in Automatic Generated Quantitative Biomarkers and Correlation with Pathology of Alzheimer’s Disease – A Longitudinal Study

Abstract

Motivation: AD-RAI is a novel MRI-based machine-learning derived biomarker and the value of longitudinal AD-RAI remains unclear. Goal(s): We aimed to assess longitudinal changes of the MRI biomarkers (i.e., AD-RAI, HV, HF, BPV, BPF) in correlation with change in time and conversion status with and without A+T+. Approach: We selected 168 CU and MCI in ADNI with four-year follow-up with serial MRI scans and corresponding CSF and used linear mixed-effects models to estimate and compare. Results: AD-RAI of subjects with A+T+ increased significantly faster than non-A+T+ over time and AD-RAI has the potential to track CSF Aβ1–42 as an effective longitudinal surrogate biomarker. Impact: If the serial AD-RAI change over time is associated with conversion status and AD pathologies. It may be used as a surrogate marker for monitoring disease progression or treatment response in AD.