Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
PLoS One. 2011;6(7):e22193. doi: 10.1371/journal.pone.0022193. Epub 2011 Jul 29.
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.
在最近的研究中，已经提出了许多单变量和多变量方法，以使用成像数据来改善各种痴呆综合征的自动分类。其中一些方法不提供将可能的混杂变量（如年龄）纳入统计评估的可能性。在临床研究中也存在类似的问题，因为并非总是可以在所有混杂变量（例如，年龄<65 岁的早发性和年龄≥65 岁的晚发性阿尔茨海默病（AD）患者）中匹配不同的临床组。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）或体素基于形态学（VBM）对磁共振成像（MRI）数据进行统计评估之前，控制混杂变量（如年龄）的可能影响。我们比较了基于 MRI 数据的 SVM 结果，以分类 80 名 AD 患者和 79 名健康对照者，其中包括和不包括年龄校正前的结果。此外，我们比较了 VBM 结果，以比较年龄不同的三组 AD 患者与同一组未包括年龄作为协变量、包括年龄作为协变量或使用本文提出的方法进行年龄校正的对照组的结果。与未校正数据相比，使用本文提出的方法进行 SVM 分类可提高组间分类准确性。此外，应用本文提出的年龄校正方法可大大改善年龄不同的 AD 患者与对照组相比使用 VBM 进行的疾病相关灰质萎缩的单变量检测。结果表明，该方法通常适用于 SVM 或 VBM 分析中控制混杂变量（如年龄）的方法。因此，该方法可能会改善和扩展这些方法在临床神经科学中的应用。
