Casanova R, Whitlow C T, Wagner B, Espeland M A, Maldjian J A
Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Open Neuroimag J. 2012;6:1-9. doi: 10.2174/1874440001206010001. Epub 2012 Jan 26.
In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.
在这项工作中，我们结合机器学习方法和图论分析来研究静息态脑网络连通性中的性别相关差异。从功能磁共振成像静息态数据计算得到的所有相关性集合被用作分类的输入特征。使用两种集成学习方法来检测脑网络组（男性与女性）之间的判别性边集：1）随机森林；2）基于最小角收缩和选择算子（套索）回归器的集成方法。置换检验不仅用于评估分类准确率的显著性，还用于评估特征选择的显著性。最后，将这些方法应用于从连接组项目网站下载的数据。我们的结果表明，脑功能的性别差异可能通过性别判别性边与特定关键节点之间的性二态区域连通性有关。
