Original Research ARTICLE
Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer’s Disease in the Aging Brain
- 1Stark Neurosciences Research Institute, School of Medicine, Indiana University, United States
- 2Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, United States
- 3Information Sciences Institute, University of Southern California, United States
Brain aging is a multifaceted process that remains poorly understood. Despite significant advances in technology, progress towards identifying reliable risk factors for suboptimal brain health requires realistically complex analytic methods to explain relationships between genetics, biology, and environment. Here we show the utility of a novel unsupervised machine learning technique - Correlation Explanation (CorEx) - to discover how individual measures from structural brain imaging, genetics, plasma, and CSF markers can jointly provide information on risk for Alzheimer’s disease (AD). We examined 829 participants (Mage: 75.3 ± 6.9 years; 350 women and 479 men) from the Alzheimer’s Disease Neuroimaging Initiative database to identify multivariate predictors of cognitive decline and brain atrophy over a one-year period. Our sample included 231 cognitively normal individuals, 397 with mild cognitive impairment (MCI), and 201 with AD as their baseline diagnosis. Analyses revealed latent factors based on data-driven combinations of plasma markers and brain metrics, that were aligned with established biological pathways in AD. These factors were able to improve disease prediction along the trajectory from normal cognition and MCI to AD, with an area under the receiver operating curve of up to 99%, and prediction accuracy of up to 89.9% on independent “held out” testing data. Further, the most important latent factors that predicted AD consisted of a novel set of variables that are essential for cardiovascular, immune, and bioenergetic functions. Collectively, these results demonstrate the strength of unsupervised network measures in the detection and prediction of AD.
Keywords: Information Theory, machine learning, Alzheiemr's disease, Neuroimaging, Plasma biomarkers
Received: 25 Sep 2018;
Accepted: 08 Nov 2018.
Edited by:Xiaobo Mao, School of Medicine, Johns Hopkins University, United States
Reviewed by:Jianmin Zhang, Chinese Academy of Medical Sciences, China
Yulan Xiong, Kansas State University, United States
Copyright: © 2018 Riedel, Daianu, Ver Steeg, Mezher, Salminen, Galstyan and Thompson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Paul M. Thompson, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, California, United States, firstname.lastname@example.org