Multimodal classification of Alzheimer's disease and mild cognitive impairment.
Multimodal classification of Alzheimer's disease and mild cognitive impairment.
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DOI：
10.1016/j.neuroimage.2011.01.008
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发表时间：
2011-04-01
期刊：
影响因子：
5.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Zhang D;Wang Y;Zhou L;Yuan H;Shen D;Alzheimer's Disease Neuroimaging Initiative
阿尔茨海默病（AD）及其前驱期（即轻度认知障碍（MCI））的有效准确诊断近年来越来越受到人们的关注。到目前为止，多种生物标志物已被证明对AD和MCI的诊断敏感，即用于脑萎缩测量的结构磁共振成像（MRI），用于低代谢量化的功能成像（如FDG-PET），以及用于定量特定蛋白质的脑脊液（CSF）。然而，尽管最近的研究表明，不同的生物标志物可能为AD和MCI的诊断提供互补的信息，但大多数现有研究只关注单一模式的AD和MCI的生物标志物诊断。在本文中，我们建议结合三种生物标志物，即MRI， FDG-PET和CSF生物标志物，使用核组合方法来区分AD（或MCI）和健康对照。具体来说，ADNI基线MRI、FDG-PET和CSF数据来自51名AD患者、99名MCI患者（包括43名在18个月内转化为AD的MCI转化者和56名在18个月内未转化为AD的MCI非转化者）和52名健康对照者，用于开发和验证我们提出的多模式分类方法。特别是，对于每个MR或FDG-PET图像，从93个感兴趣区域（roi）中提取93个体积特征，并通过地图集扭曲算法自动标记。对于脑脊液生物标志物，直接使用其原始值作为特征。然后，采用线性支持向量机（SVM）对分类精度进行10倍交叉验证。因此，在将AD与健康对照进行分类时，当结合所有三种生物标志物模式时，我们的分类准确率为93.2%（灵敏度为93%，特异性为93.3%），而当使用最好的生物标志物个体模式时，分类准确率仅为86.5%。同样，对于从健康对照中对MCI进行分类，我们的联合方法的分类准确率为76.4%（灵敏度为81.8%，特异性为66%），即使使用生物标志物的最佳个体模式，分类准确率也只有72%。进一步分析该方法的MCI敏感性表明，91.5%的MCI转换器和73.4%的MCI非转换器被正确分类。此外，我们还在使用特征选择方法选择最具判别性的MR和FDG-PET特征时评估了分类性能。同样，与使用单个生物标记物的情况相比，我们的组合方法显示出更好的性能。
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 attentions recently. So far, multiple biomarkers have been shown 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 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.
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DOI：
10.1093/brain/awp123
发表时间：
2009-08
期刊：
Brain : a journal of neurology
影响因子：
--
作者：
Desikan RS;Cabral HJ;Hess CP;Dillon WP;Glastonbury CM;Weiner MW;Schmansky NJ;Greve DN;Salat DH;Buckner RL;Fischl B;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
14
作者：
Brookmeyer, Ron;Johnson, Elizabeth;Arrighi, H. Michael
通讯作者：
Arrighi, H. Michael
影响因子：
2
作者：
Higdon, R;Foster, NL;Minoshima, S
通讯作者：
Minoshima, S
影响因子：
0.8
作者：
Chételat, G;Eustache, F;Desgranges, B
通讯作者：
Desgranges, B
影响因子：
4.2
作者：
Diehl, J;Grimmer, T;Kurz, A
通讯作者：
Kurz, A