Meiyan Huang, Southern Medical University, China
Wei Yang, Southern Medical University, China
Qianjin Feng, Southern Medical University, China
Wufan Chen, Southern Medical University, China
Michael Weiner, University of California - San Francisco
Paul Aisen, University of California
Ronald Petersen, Mayo Clinic
Clifford R. Jack Jr., Mayo Clinic
William Jagust, University of California - Berkeley
John Trojanowki, University of Pennsylvania
Arthur W. Toga, University of Southern California
Laurel Beckett, University of California - Davis
Robert C. Green, Harvard University
Andrew Saykin, Indiana University
John Morris, Washington University in St. Louis
Leslie M. Shaw, Washington University in St. Louis
Jeffrey Kaye, Oregon Health and Science University
Joseph Quinn, Oregon Health and Science University
Lisa Silbert, Oregon Health and Science University
Betty Lind, Oregon Health and Science University
Raina Carter, Oregon Health and Science University
Sara Dolen, Oregon Health and Science University
Lon S. Schneider, University of Southern California
Sonia Pawluczyk, University of Southern California
Mauricio Beccera, University of Southern California
Liberty Teodoro, University of Southern California
Bryan Spann, University of Southern California
James Brewer, University of California - San Diego
Helen Vanderswag, University of California - San Diego
Adam Fleisher, University of California - San Diego
Charles D. Smith, University of KentuckyFollow
Greg A. Jicha, University of KentuckyFollow
Peter A. Hardy, University of KentuckyFollow
Partha Sinha, University of Kentucky
Elizabeth Oates, University of KentuckyFollow
Gary Conrad, University of KentuckyFollow


Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.

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Notes/Citation Information

Published in Scientific Reports, v. 7, article no. 39880, p. 1-13.

© The Author(s) 2017

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit

Due to the large number of authors, only the first 30 and the authors affiliated with the University of Kentucky are listed in the author section above. For the complete list of authors, please download this article or visit:

This group of authors is collectively known as the Alzheimer’s Disease Neuroimaging Initiative.

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Funding Information

This work was supported by the Science and Technology Planning Project of Guangdong Province, China (grant number 2015B010131011); Major Program of National Natural Science Foundation of China (grant number U15012561016942); and National Natural Science Funds of China (NSFC, grant number 31371009 and 81601562). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, P fizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the US Food and Drug Administration.