Pathway-based classification of brain activities for alzheimer's disease analysis

Abstract

The advent of resting-state (RS) functional magnetic resonance imaging (fMRI) technology has made it possible to classify Alzheimer's disease (AD) states based on the quantitative activity indices of brain regions. Current connectivity-based classification techniques suffer from limited reproducibility due to the need for prior knowledge on discriminative brain regions and intrinsic heterogeneity in the course of AD progression. Actually, similar challenges have been already addressed in molecular bioinformatics communities. They have achieved higher and reproducible classification accuracy and have identified interpretable markers by incorporating molecular pathway information in their classification. We have adopted a similar strategy to the RS-fMRI-based AD classification problem. After collecting various functional brain pathways from literature, we have quantified which pathways show significantly different activity levels between AD patients and healthy subjects. Moreover, discriminatory pathways between AD patients and healthy subjects may facilitate the interpretation of functional alterations in the course of AD progression.