Age correction in dementia--matching to a healthy brain.
Age correction in dementia--matching to a healthy brain.
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DOI：
10.1371/journal.pone.0022193
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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：
Dukart J;Schroeter ML;Mueller K;Alzheimer's Disease Neuroimaging Initiative
在最近的研究中，已经提出了许多单变量和多变量方法来改进使用成像数据对各种痴呆综合征的自动分类。其中一些方法无法将年龄等可能的混杂变量整合到统计评估中。临床研究中有时也存在类似的问题，因为并不总是能够在所有混杂变量中将不同的临床组相互匹配，例如早发（年龄<65岁）和晚发（年龄≥65）阿尔茨海默病（AD）患者。在这里，我们提出了一种简单的方法，在使用支持向量机分类（SVM）或基于体素的形态测量（VBM）对磁共振成像（MRI）数据进行统计评估之前，控制年龄等混杂变量可能产生的影响。我们比较了基于 MRI 数据（有或没有事先年龄校正）的 80 名 AD 患者和 79 名健康对照受试者的 SVM 分类结果。此外，我们还比较了三组不同年龄的 AD 患者与未将年龄作为协变量、以年龄作为协变量或使用所提出的方法进行先前年龄校正而获得的同一组对照受试者的 VBM 结果。与未校正的数据相比，使用所提出的方法进行 SVM 分类具有更高的组间分类精度。此外，应用所提出的年龄校正大大改善了使用 VBM 对年龄与对照受试者不同的 AD 患者进行的疾病相关灰质萎缩的单变量检测。结果表明，这项工作中提出的方法通常适合控制 SVM 或 VBM 分析中的年龄等混杂变量。因此，该方法可能会改进和扩展这些方法在临床神经科学中的应用。
In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.
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影响因子：
5.7
作者：
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通讯作者：
Frackowiak, Richard S. J.
影响因子：
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作者：
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通讯作者：
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