Cuingnet Rémi, Rosso Charlotte, Lehéricy Stéphane, Dormont Didier, Benali Habib, Samson Yves, Colliot Olivier
Université Pierre et Marie Curie-Paris 6, CNRS UMR 7225, Inserm UMR_S 975, Centre de Recherche de l'Institut Cerveau-Moelle (CRICM), Paris, France.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):316-23. doi: 10.1007/978-3-642-15705-9_39.
This paper introduces a new method to detect group differences in brain images based on spatially regularized support vector machines (SVM). First, we propose to spatially regularize the SVM using a graph encoding the voxels' proximity. Two examples of regularization graphs are provided. Significant differences between two populations are detected using statistical tests on the margins of the SVM. We first tested our method on synthetic examples. We then applied it to 72 stroke patients to detect brain areas associated with motor outcome at 90 days, based on diffusion-weighted images acquired at the acute stage (one day delay). The proposed method showed that poor motor outcome is associated to changes in the corticospinal bundle and white matter tracts originating from the premotor cortex. Standard mass univariate analyses failed to detect any difference.
本文介绍了一种基于空间正则化支持向量机（SVM）检测脑图像中组间差异的新方法。首先，我们建议使用一个编码体素邻近性的图对SVM进行空间正则化。提供了两个正则化图的示例。使用SVM边界上的统计检验来检测两个群体之间的显著差异。我们首先在合成示例上测试了我们的方法。然后将其应用于72名中风患者，基于急性期（延迟一天）获取的扩散加权图像，检测90天时与运动结果相关的脑区。所提出的方法表明，运动结果不佳与皮质脊髓束和源自运动前皮层的白质束的变化有关。标准的质量单变量分析未能检测到任何差异。
