Capturing Morphometric Patterns of Alzheimer's Disease Progression with a TV‐L1 Ordinal Machine Learning Model

The most common neurodegenerative disease, Alzheimer’s disease (AD) constitutes about two thirds of cases of dementia overall [1]. Mild cognitive impairment (MCI) is considered to be a transitional stage between aging and AD [2]. This general AD staging information and ample MCI data motivate a further investigation on the subcortical shape analysis incorporating MCI data in AD study. We therefore designed a novel logistic regression model to explore more morphometric patterns associated with the disease progression of AD.