Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.
Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.
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DOI：
10.1016/j.neuroimage.2018.01.065
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发表时间：
2018-05-15
期刊：
影响因子：
5.7
通讯作者：
Clark KA
中科院分区：
文献类型：
--
作者：
Sepehrband F;Lynch KM;Cabeen RP;Gonzalez-Zacarias C;Zhao L;D'Arcy M;Kesselman C;Herting MM;Dinov ID;Toga AW;Clark KA
使用多变量统计学习方法探索神经解剖学上的性别差异可以产生单变量分析无法得出的见解。虽然大脑总体积的总体差异是众所周知的，但要揭示神经解剖学中更细微的、与性别相关的区域性差异，需要一种多变量方法，能够准确地模拟空间复杂性以及神经解剖特征之间的相互作用。在这里，我们开发了一个使用支持向量机(支持向量机)分类器的多变量统计学习模型，以根据来自费城神经发育队列(PNC)的967名健康青年的单点研究的MRI得出的区域神经解剖特征来预测性别。然后，我们在来自多点儿科成像、神经认知和遗传学(PING)队列研究的682名健康青年的独立数据集上验证了多变量模型。训练后的模型显示出83%的交叉验证预测准确率，并从独立的多站点数据集中正确地预测了77%的受试者的性别。结果表明，枕中叶皮质厚度和角回是性别的主要预测因子。结果还表明，超越经典回归方法，捕捉大脑特征之间的相互作用，以便更好地表征男性和女性青年的性别差异，可以带来推断上的好处。我们还确定了特定的皮质形态测量和分割技术，例如源自Destrieux图谱的皮质厚度，与其他脑图谱(Desikan-Killiany、Brodmann和皮质下图谱)相比，它们能够更好地区分男性和女性。
Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).
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