Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease


Meiyan Huang, Southern Medical University
Wei Yang, Southern Medical University
Qianjin Feng, Southern Medical University
Wufan Chen, Southern Medical University
Michael W. Weiner, University of California, San Francisco
Paul Aisen, UC San Diego School of Medicine
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, University of Southern California
Laurel Beckett, University of California, Davis
Robert C. Green, Brigham and Women's Hospital
Andrew J. Saykin, Indiana University Bloomington
John C. Morris, Washington University in St. Louis
Leslie M. Shaw, Washington University in St. Louis
Jeffrey Kaye, Oregon Health & Science University
Joseph Quinn, Oregon Health & Science University
Lisa Silbert, Oregon Health & Science University
Betty Lind, Oregon Health & Science University
Raina Carter, Oregon Health & Science University
Sara Dolen, Oregon Health & 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 M. 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 L. Heidebrink, University of Michigan, Ann Arbor
Joanne L. Lord, University of Michigan, Ann Arbor

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Scientific Reports



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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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