Group-constrained Laplacian Eigenmaps: Longitudinal AD biomarker learning

Abstract

Longitudinal modeling of biomarkers to assess a subject’s risk of developing Alzheimers disease (AD) or determine the current state in the disease trajectory has recently received increased attention. Here, a new method to estimate the time-to-conversion (TTC) of mild cognitive impaired (MCI) subjects to AD from a low-dimensional representation of the data is proposed. This is achieved via a combination of multi-level feature selection followed by a novel formulation of the Laplacian Eigenmaps manifold learning algorithm that allows the incorporation of group constraints. Feature selection is performed using Magnetic Resonance (MR) images that have been aligned at different detail levels to a template. The suggested group constraints are added to the construction of the neighborhood matrix which is used to calculate the graph Laplacian in the Laplacian Eigenmaps algorithm. The proposed formulation yields relevant improvements for the prediction of the TTC and for the three-way classification (control/MCI/AD) on the ADNI database.