Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors.
Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors.
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DOI：
10.1371/journal.pone.0021896
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Cui Y;Liu B;Luo S;Zhen X;Fan M;Liu T;Zhu W;Park M;Jiang T;Jin JS;Alzheimer's Disease Neuroimaging Initiative
预测轻度认知障碍（MCI）向阿尔茨海默病（AD）的转化是AD研究的主要兴趣。已经提出了大量的潜在预测因子，大多数研究倾向于检查一个或一组相关的预测因子。在这项研究中，我们同时检查了来自不同模式数据的多个特征，包括结构磁共振成像（MRI）形态测量，脑脊液（CSF）生物标志物和神经心理学和功能测量（NM），以探索阿尔茨海默病神经影像学倡议（ADNI）队列中从MCI转换为AD的最佳预测因子。在FreeSurfer衍生的MRI特征提取、CSF和NM特征收集之后，采用特征选择来从每种模态中选择最佳特征子集。支持向量机（SVM）分类器，然后训练正常对照（NC）和AD参与者。测试在MCIc（在24个月内转化为AD的MCI个体）和MCInc（在24个月内未转化为AD的MCI个体）组中进行。分类结果表明NM优于CSF和MRI特征。选择的NM，MRI和CSF特征的组合达到了67.13%的准确性，96.43%的敏感性，48.28%的特异性，和AUC（曲线下面积）为0.796。对不同随访评估时转换的MCIC预测值的分析表明，12个月内转换的个体与12个月后转换的个体之间的预测值存在显著差异。这项研究建立了有意义的多变量预测因素，包括选定的NM，MRI和CSF的措施，这可能是有用的和实用的临床诊断。
Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.
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影响因子：
5.7
作者：
Costafreda, Sergi G.;Dinov, Ivo D.;Tu, Zhuowen;Shi, Yonggang;Liu, Cheng-Yi;Kloszewska, Iwona;Mecocci, Patrizia;Soininen, Hilkka;Tsolaki, Magda;Vellas, Bruno;Wahlund, Lars-Olof;Spenger, Christian;Toga, Arthur W.;Lovestone, Simon;Simmons, Andrew
通讯作者：
Simmons, Andrew
DOI：
10.3174/ajnr.a1397
发表时间：
2009-03
期刊：
AJNR. American journal of neuroradiology
影响因子：
--
作者：
Desikan RS;Cabral HJ;Fischl B;Guttmann CR;Blacker D;Hyman BT;Albert MS;Killiany RJ
通讯作者：
Killiany RJ
影响因子：
4.2
作者：
Desikan RS;Cabral HJ;Settecase F;Hess CP;Dillon WP;Glastonbury CM;Weiner MW;Schmansky NJ;Salat DH;Fischl B;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
38.1
作者：
Frisoni, Giovanni B.;Fox, Nick C.;Jack, Clifford R., Jr.;Scheltens, Philip;Thompson, Paul M.
通讯作者：
Thompson, Paul M.
影响因子：
5.7
作者：
Fischl, B;Salat, DH;Dale, AM
通讯作者：
Dale, AM