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首页> 外文期刊>Psychiatry Research. Neuroimaging >Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines
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Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines

机译：使用基于图集的扩散加权成像和支持向量机对小儿双相情感障碍进行预测分类

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Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from healthy controls with a high specificity and sensitivity. Diffusion-weighted imaging scans were acquired from 16 youths diagnosed with DSM-IV bipolar disorder and 16 demographically matched healthy controls. Regional white matter tissue microstructural measurements such as fractional anisotropy, axial diffusivity and radial diffusivity were computed using an atlas-based approach. These measurements were used to 'train' a support vector machine (SVM) algorithm to predict new or 'unseen' subjects' diagnostic labels. The SVM algorithm predicted individual subjects with specificity=87.5%, sensitivity=68.75%, accuracy=78.12%, positive predictive value=84.62%, negative predictive value=73.68%, area under receiver operating characteristic curve (AUROC)=0.7812 and chi-square p-value=0.0012. A pattern of reduced regional white matter fractional anisotropy was observed in pediatric bipolar disorder patients. These results suggest that atlas-based diffusion weighted imaging measurements can distinguish individual pediatric bipolar disorder patients from healthy controls. Notably, from a clinical perspective these findings will contribute to the pathophysiological understanding of pediatric bipolar disorder. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：先前的研究报道了小儿双相情感障碍的白色物质扩散异常。但是，尚未确定这些异常是否能够以高特异性和敏感性将小儿双相情感障碍的个体受试者与健康对照区分开。扩散加权成像扫描来自16位被诊断为DSM-IV双相情感障碍的青年和16位人口统计学匹配的健康对照。使用基于图集的方法计算了局部白质组织的微结构测量值，例如分数各向异性，轴向扩散率和径向扩散率。这些测量结果用于“训练”支持向量机（SVM）算法，以预测新的或“看不见的”受试者的诊断标签。 SVM算法可预测个体，其特异性为87.5％，灵敏度为68.75％，准确度为78.12％，阳性预测值为84.62％，阴性预测值为73.68％，受试者工作特征曲线下面积（AUROC）为0.7812，chi- p值平方= 0.0012。在儿童双相情感障碍患者中观察到区域白质分数各向异性降低的模式。这些结果表明，基于图集的弥散加权成像测量可以将小儿双相情感障碍患者与健康对照区分开。值得注意的是，从临床角度来看，这些发现将有助于对小儿双相情感障碍的病理生理理解。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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