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首页> 外文会议>Asilomar Conference on Signals, Systems, and Computers >Classifying Adolescent Major Depressive Disorder using Linear SVM with Anatomical Features from Diffusion Weighted Imaging
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Classifying Adolescent Major Depressive Disorder using Linear SVM with Anatomical Features from Diffusion Weighted Imaging

机译：使用线性SVM分类青少年主要抑郁症，从扩散加权成像解剖学特征

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Adolescence is a period of rapid brain maturation and a critical period for the onset of Major Depressive Disorder (MDD) that usually leads to serious outcomes such as suicide. Although changes in anatomical connectivity in MDD have been reported, changes in network topology for MDD remain unclear. Additionally, whether the changes are the same for adolescent MDD and adult MDD remains unclear as well. This paper explores anatomical features including: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks, and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. In addition to statistical tests, univariate classifiers are designed to evaluate the discriminating power of features. Furthermore, multivariate classifiers are trained for distinguishing healthy subjects from MDD patients. The best classifier achieves an accuracy of 76.56%, 81.08% sensitivity, 70.37% specificity and 78.95% precision for 64 subjects (37 MDD and 27 matched healthy control). The selected features include: 1) betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, 2) participation coefficient of the right pars opercularis of the AD network at 16% sparsity, 3) participation coefficient of the left insular cortex of the MD network at 21% sparsity, and 4) participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. These features reflect changes in the topological structure of the brain anatomical network in MDD.
机译：青春期是一种快速脑成熟的时期，以及主要抑郁症（MDD）发作的关键时期，通常导致自杀等严重结果。虽然已经报告了MDD中解剖连接的变化，但MDD的网络拓扑变化仍然不清楚。另外，对于青少年的变化是相同的，因为青少年MDD和成人MDD也不清楚。本文探讨了解剖特征，包括：a）通过解剖网络的一对脑区域和b）拓扑测量之间的扩散张量成像测量来定义的解剖学连接，并施加机器学习方法来识别区分MDD患者免受健康受试者的响应生物标志物。除统计测试外，单变量分类机旨在评估特征的辨别力。此外，对MDD患者的健康受试者培训多变量分类器。最好的分类器可实现76.56±0.08％，灵敏度，70.37 ％特异性和78.95±78.95 ％精度的精度（37 mdd和27个匹配的健康控制）。所选功能包括：1）ADC网络的右侧旋转的中心地位与12 ％稀疏，2）右侧的右侧右侧的参与系数在16 ％稀疏，3）左边的参与系数。 MD网络的绝大皮质在21 ％稀疏度下，4）在ADC网络中的右侧横向跨越式皮质的参与系数为10 ％稀疏性。这些特征反映了MDD中脑解剖网络的拓扑结构的变化。

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