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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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.
Huang, Meiyan; Yang, Wei; Feng, Qianjin; Chen, Wufan; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R. Jr.; Jagust, William; Trojanowki, John; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Saykin, Andrew; Morris, John; Shaw, Leslie M.; Kaye, Jeffrey; Quinn, Joseph; Silbert, Lisa; Lind, Betty; Carter, Raina; Dolen, Sara; Schneider, Lon S.; Pawluczyk, Sonia; Beccera, Mauricio; Teodoro, Liberty; Spann, Bryan; Brewer, James; Vanderswag, Helen; Fleisher, Adam; Smith, Charles D.; Jicha, Greg A.; Hardy, Peter A.; Sinha, Partha; Oates, Elizabeth; and Conrad, Gary, "Longitudinal Measurement and Hierarchical Classification Framework for the Prediction of Alzheimer's Disease" (2017). Neurology Faculty Publications. 18.