Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach
Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach
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DOI：
10.1016/j.neuroimage.2009.08.024
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发表时间：
2010-01-01
期刊：
影响因子：
5.7
通讯作者：
Murphy, Declan G.
中科院分区：
文献类型：
--
作者：
Ecker, Christine;Rocha-Rego, Vanessa;Murphy, Declan G.
自闭症谱系障碍（ASD）伴随着细微的、空间分布的脑解剖差异，这些差异很难用传统的质量单变量方法（如VBM）检测到。这些需要对多次比较进行校正，因此需要相对较大的样本来获得足够的统计能力。因此，来自相对较小的研究的神经解剖学差异报告是高度可变的。此外，VBM不具有预测价值，限制了其诊断价值。在这里，我们使用支持向量机（SVM）的全脑分类方法检查了与ASD相关的神经解剖学网络，并研究了结构MRI扫描对成人ASD的预测价值。随后，将SVM与VBM的结果进行比较。我们纳入了44名成年男性；22名被诊断为ASD的患者使用了“黄金标准”的研究访谈和22名健康匹配的对照。支持向量机识别空间分布网络，区分ASD和对照组。这些系统包括边缘系统、额纹状体系统、额颞系统、额顶叶系统和小脑系统。支持向量机应用于灰质扫描正确分类ASD个体，特异性为86.0%，敏感性为88.0%。68.0%的病例通过白质解剖正确分类。与分离超平面的距离（即测试边界）与当前症状严重程度显著相关。相比之下，VBM在常规统计严格程度上显示组间差异不大。因此，我们认为支持向量机可以检测ASD成人和对照组之间大脑网络的细微和空间分布差异。此外，这些差异为群体成员提供了显著的预测能力，这与症状严重程度有关。(c) 2009爱思唯尔公司版权所有。
Autistic spectrum disorder (ASD) is accompanied by subtle and spatially distributed differences in brain anatomy that are difficult to detect using conventional mass-univariate methods (e.g., VBM). These require correction for multiple comparisons and hence need relatively large samples to attain sufficient statistical power. Reports of neuroanatomical differences from relatively small studies are thus highly variable. Also, VBM does not provide predictive value, limiting its diagnostic value.Here, we examined neuroanatomical networks implicated in ASD using a whole-brain classification approach employing a support vector machine (SVM) and investigated the predictive Value of structural MRI scans in adults with ASD. Subsequently, results were compared between SVM and VBM. We included 44 male adults; 22 diagnosed with ASD using "gold-standard" research interviews and 22 healthy matched controls.SVM identified spatially distributed networks discriminating between ASD and controls. These included the limbic, frontal-striatal, fronto-temporal, fronto-parietal and cerebellar systems. SVM applied to gray matter scans correctly classified ASD individuals at a specificity of 86.0% and a sensitivity of 88.0%. Cases (68.0%) were correctly classified using white matter anatomy. The distance from the separating hyperplane (i.e., the test margin) was significantly related to current symptom severity. In contrast, VBM revealed few significant between-group differences at conventional levels of statistical stringency.We therefore suggest that SVM can detect subtle and spatially distributed differences in brain networks between adults with ASD and controls. Also, these differences provide significant predictive power for group membership, which is related to symptom severity. (c) 2009 Elsevier Inc. All rights reserved.