Asha Singanamalli, Case Western Reserve University
Haibo Wang, Case Western Reserve University
Anant Madabhushi, Case Western Reserve University
Michael Weiner, University of California - San Francisco
Paul Aisen, University of California
Ronald Petersen, Mayo Clinic
Clifford Jack, Mayo Clinic
William Jagust, University of California - Berkeley
John Trojanowki, University of Pennsylvania
Arthur Toga, University of Southern California
Laurel Beckett, University of California - Davis
Robert Green, Harvard University
Andrew Saykin, Indiana University
John Morris, Washington University in St. Louis
Leslie 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 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
Judith Heidebrink, University of Michigan
Charles 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


The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer's Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade. CaMCCo is evaluated on a data cohort comprising 149 patients for whom neurophysiological, neuroimaging, proteomic and genomic data were available. Results suggest that fusion of select modalities for each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68). In addition, CaMCCo outperforms all other multi-class classification methods for MCI prediction (PPV: 0.80 vs. 0.67, 0.63).

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Notes/Citation Information

Published in Scientific Reports, v. 7, article no. 8137, p. 1-14.

© The Author(s) 2017

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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

Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R21CA179327-01, R21CA195152-01, the National Institute of Diabetes and Digestive and Kidney Diseases under award number R01DK098503-02, National Center for Research Resources under award number 1 C06 RR12463-01, the DOD Prostate Cancer Synergistic Idea Development Award (PC120857); the DOD Lung Cancer Idea Development New Investigator Award (LC130463), the DOD Prostate Cancer Idea Development Award, the DOD Peer Reviewed Cancer Research Program W81XWH-16-1-0329; the Case Comprehensive Cancer Center Pilot Grant VelaSano Grant from the Cleveland Clinic the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University.

Due to the large number of funding sources, only the first few are listed in this section. For the complete list of funding sources, please download this article.

Related Content

Supplementary information accompanies this paper at doi: 10.1038/s41598-017-03925-0

41598_2017_3925_MOESM1_ESM.pdf (47 kB)
Supplementary Information