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首页> 外文期刊>Psychiatry Research. Neuroimaging >Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder
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Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

机译：评估应用机器学习算法在具有重大抑郁症的个体扩散张量MRI措施的诊断效用

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Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.
机译：利用MRI诊断精神障碍一直是一个长期目标。尽管如此，之前的绝大多数神经成像工作都是描述性的，而不是预测性的。目前的研究将支持向量机（SVM）学习应用于脑白质的MRI测量，对患有重度抑郁症（MDD）的成年人和健康对照组进行分类。在一组精确匹配的MDD患者（n=25）和健康对照组（n=25）中，SVM学习准确地（74%）在脑白质分数各向异性值（FA）地图上对患者和对照组进行分类。该研究揭示了三个主要发现：1）将支持向量机应用于DTI衍生的FA图谱，可以准确地将MDD与健康对照组进行分类；2） 当只检查右半球白质时，预测能力最强；3）从MDD和健康对照组之间通过单变量对比确定为显著不同的区域移除FA值不会改变SVM的准确性。这些结果表明，应用于神经成像数据的SVM学习可以对MDD的存在与否进行分类，并且预测信息分布在整个大脑网络中，而不是高度局部化。最后，通过典型的单变量对比揭示的MDD组差异不一定揭示提供准确预测信息的模式。

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