Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.
Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.
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DOI：
10.1111/desc.12407
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发表时间：
2016-07
影响因子：
3.7
通讯作者：
Schlaggar BL
中科院分区：
文献类型：
--
作者：
Greene DJ;Church JA;Dosenbach NU;Nielsen AN;Adeyemo B;Nardos B;Petersen SE;Black KJ;Schlaggar BL
抽动秽语综合征（TS）是一种以运动和发声抽动为特征的发育性神经精神障碍。TS患者将大大受益于症状时程预测和治疗效果的进步。作为第一步，我们应用了一种多变量方法-支持向量机（SVM）分类-来测试用静息状态功能连接（RSFC）MRI测量的大脑网络活动模式是否可以预测个体的诊断组成员资格。纳入了42名TS儿童（8-15岁）和42名未受影响的对照（年龄、智商、扫描仪内运动匹配）的RSFC数据。虽然单变量检验没有发现显著的组间差异，但SVM分类组成员资格的准确率约为70%（p < .001）。我们还报告了一种新的适应SVM的二进制分类，除了整体准确率的SVM，提供了一个信心的措施，每个人的准确分类。我们的研究结果支持了这样一种观点，即多变量方法可以更好地捕捉某些脑部疾病的复杂性，并有望预测TS患者的预后和治疗结果。
Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method – support vector machine (SVM) classification – to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8–15 yrs) and 42 unaffected controls (age, IQ, in‐scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.