TY - JOUR
T1 - Multimodal classification of Alzheimer's disease and mild cognitive impairment
AU - Zhang, Daoqiang
AU - Wang, Yaping
AU - Zhou, Luping
AU - Yuan, Hong
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grants EB006733 , EB008374 , EB009634 and MH088520 .
Funding Information:
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 , Pfizer 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 U.S. Food and Drug Administration . Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.
Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011/4/1
Y1 - 2011/4/1
N2 - Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51. AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18. months and 56 MCI non-converters who had not converted to AD within 18. months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.
AB - Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51. AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18. months and 56 MCI non-converters who had not converted to AD within 18. months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.
KW - AD biomarkers
KW - Alzheimer's disease (AD)
KW - CSF
KW - MCI
KW - MRI
KW - Multimodal classification
KW - PET
UR - http://www.scopus.com/inward/record.url?scp=79952073234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952073234&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2011.01.008
DO - 10.1016/j.neuroimage.2011.01.008
M3 - Article
C2 - 21236349
AN - SCOPUS:79952073234
VL - 55
SP - 856
EP - 867
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - 3
ER -