Manifold alignment and transfer learning for classification of alzheimer’s disease

Abstract

Magnetic resonance (MR) images acquired at different field strengths have different intensity appearance and thus cannot be easily combined into a single manifold space. A framework to learn a joint low-dimensional representation of brain MR images, acquired either at 1.5 or 3 Tesla, is proposed. In this manifold subspace, knowledge can be shared and transfered between the two distinct but related datasets. The joint manifold subspace is built using an adaptation of Laplacian eigenmaps (LE) from a data-driven region of interest (ROI). The ROI is learned using sparse regression to perform simultaneous variable selection at multiple levels of alignment to the MNI152 template. Additionally, a stability selection re-sampling scheme is used to reduce sampling bias while learning the ROI. Knowledge about the intrinsic embedding coordinates of different instances, common to both feature spaces, is used to constrain their alignment in the joint manifold. Alzheimer’s Disease (AD) classification results obtained with the proposed approach are presented using data from more than 1500 subjects from ADNI-1, ADNI-GO and ADNI-2 datasets. Results calculated using the learned joint manifold in general outperform those obtained in each independent manifold. Accuracies calculated on ADNI-1 are comparable to other state-of-the-art approaches. To our knowledge, classification accuracies have not been reported before on the complete ADNI (-1, -GO and -2) cohort combined.