Department of Neuroscience, University of California Los Angeles, Los Angeles, CA, USA.
Neuroimage. 2011 May 15;56(2):788-96. doi: 10.1016/j.neuroimage.2010.04.273. Epub 2010 May 6.
The development of MRI measures as biomarkers for neurodegenerative disease could prove extremely valuable for the assessment of neuroprotective therapies. Much current research is aimed at developing such biomarkers for use in people who are gene-positive for Huntington's disease yet exhibit few or no clinical symptoms of the disease (pre-HD). We acquired structural (T1), diffusion weighted and functional MRI (fMRI) data from 39 pre-HD volunteers and 25 age-matched controls. To determine whether it was possible to decode information about disease state from neuroimaging data, we applied multivariate pattern analysis techniques to several derived voxel-based and segmented region-based datasets. We found that different measures of structural, diffusion weighted, and functional MRI could successfully classify pre-HD and controls using support vector machines (SVM) and linear discriminant analysis (LDA) with up to 76% accuracy. The model producing the highest classification accuracy used LDA with a set of six volume measures from the basal ganglia. Furthermore, using support vector regression (SVR) and linear regression models, we were able to generate quantitative measures of disease progression that were significantly correlated with established measures of disease progression (estimated years to clinical onset, derived from age and genetic information) from several different neuroimaging measures. The best performing regression models used SVR with neuroimaging data from regions within the grey matter (caudate), white matter (corticospinal tract), and fMRI (insular cortex). These results highlight the utility of machine learning analyses in addition to conventional ones. We have shown that several neuroimaging measures contain multivariate patterns of information that are useful for the development of disease-state biomarkers for HD.
MRI 测量方法作为神经退行性疾病的生物标志物的发展对于神经保护疗法的评估可能非常有价值。目前许多研究旨在开发此类生物标志物，用于那些亨廷顿病基因阳性但尚无疾病（预 HD）临床症状或症状很少的人。我们从 39 名预 HD 志愿者和 25 名年龄匹配的对照者中获得了结构（T1）、弥散加权和功能 MRI（fMRI）数据。为了确定是否可以从神经影像学数据中解码疾病状态信息，我们将多元模式分析技术应用于几个基于体素和基于分割的区域衍生数据集。我们发现，使用支持向量机（SVM）和线性判别分析（LDA），不同的结构、弥散加权和功能 MRI 测量方法可以成功地对预 HD 和对照组进行分类，准确率高达 76%。产生最高分类准确率的模型使用 LDA 结合一组来自基底节的六个容积测量值。此外，使用支持向量回归（SVR）和线性回归模型，我们能够从几种不同的神经影像学测量中生成与疾病进展的既定测量（从年龄和遗传信息推断的临床发病估计年限）显著相关的疾病进展的定量测量值。表现最佳的回归模型使用 SVR 结合灰质（尾状核）、白质（皮质脊髓束）和 fMRI（脑岛皮层）内的神经影像学数据。这些结果强调了机器学习分析的除了传统分析之外的效用。我们已经表明，几种神经影像学测量方法包含有用的多变量信息模式，可用于开发 HD 的疾病状态生物标志物。
