Abstract

Motivation: To investigate the potential of macromolecule (MM) from 1HMRS as biomarkers for Alzheimer's Disease (AD). Goal(s): Enhance the MRS-only diagnostic prediction for the AD continuum by incorporating MM data. Approach: We predict the diagnosis of 143 individuals ranging from cognitively healthy to AD using only 1HMRS data, employing OPLSDA. We compare the model's performance with/without MM and validate the results with a second ML classifier. We also evaluate variable importance in classification. Results: The inclusion of MM signals improves AD diagnosis prediction when OPLSDA is used. Various MM peaks contribute to the classification. However, the transitional stage of MCI cannot be accurately classified. Impact: When combined with the appropriate method, MM signals can enhance the diagnosis of AD using MRS as a stand-alone marker, and important MM peaks belonging to the AD neurochemical fingerprint were identified.