Early Alzheimer’s disease progression detectionusing multi-subnetworks of the brain

Abstract

Alzheimer’s disease is neurodegenerative disorder believed to affect 24.3 million people worldwide. Pro- posed MRI based disease progression markers have shown ability to perform the classification between the Alzheimer’s Disease (AD), Mild Cognitive Impariment (MCI) and Normal Cognitive (NC) subjects. We exploited two approaches, first one is to use single sub-network volume as a feature, second to use a network of most discriminative sub-networks. Multi-feature approach showed improvement by 4.5% in AD/NC classification case, and 1.5% in MCI/NC case. Study was summarized for 48 AD, 119 MCI and 66 NC subjects.