Gene Expression Based Computation Methods for Alzheimer's Disease Progression using Hippocampal Volume Loss and MMSE Scores

Abstract

Personalized relevance parameterization methods (PReP-AD) based on artificial intelligence computation techniques are introduced to investigate the impact of gene expressions on Alzheimer's disease (AD) progression. Our PReP-AD methods make use of the expressions of the genes that affect AD-related protein biomarkers (e.g., Aβ1-42 and tau proteins), mini mental state examination (MMSE) scores and hippocampal volume measurements from ADNI database for the patients with mild cognitive impairment (MCI), an intermediate stage from normal cognition to AD. For MCI patients, disease progression is computed with PReP-AD-MMSE and PReP-AD-HVL methods, where the former utilizes the change in MMSE scores and the latter based on the rate of hippocampal volume loss over time. The performance of both methods are assessed with an algorithm implemented using leave-one-out-cross-validation (LOOCV). The cognitive changes of AD patients with MCI stage are detected with both our MMSE score and hippocampal volume based computation methods. We observe an average error rate of 4.8% with PReP-AD-MMSE over a 72-month period and 1.63% with PReP-AD-HVL over 12 months. The promising results indicate that artificial intelligence based computation methods can be utilized to build decision support tools for AD progression.