In this article, we study possible relations between the structural connectome and cognitiveprofiles using a multi-response nonparametric regression model under group sparsity. The aim is to identify the brain regions having significant effect on cognitive functioning. In this article, we consider nine different attributesfor each brain region as our predictors. Here, each node thus correspond to a collectionof nodal attributes. Hence, these nodal graph metrics may naturally be grouped togetherfor each node, motivating us to introduce group sparsity for feature selection. We proposeGaussian RBF-nets with a novel group sparsity inducing prior to model the unknown meanfunctions. The covariance structure of the multivariate response is characterized in termsof a linear factor modeling framework. For posterior computation, we develop an efficientMarkov chain Monte Carlo sampling algorithm. We show that the proposed method per-forms overwhelmingly better than all its competitors. Applying our proposed method to aHuman Connectome Project (HCP) dataset, we identify the important brain regions andnodal attributes for cognitive functioning, as well as identify interesting low-dimensionaldependency structures among the cognition related test scores.
翻译:在本篇文章中,我们利用群聚下的多反应非参数回归模型,研究结构连接体和认知特征之间的可能关系。目的是确定对认知功能有重大影响的大脑区域。在本篇文章中,我们把每个大脑区域的九个不同属性视为我们的预测器。这里,每个节点因此与节点属性的集合相对应。因此,这些节点图形指标可以自然地为每个节点组合在一起,从而激励我们引入集聚性特征选择。我们提议Gaussian RBF-nets,在模拟未知的中位功能之前,以新的群群聚集性为诱导。多变量反应的共变结构以线性要素建模框架为特征。对于后端计算,我们开发了一个高效的Markov链 Monte Carlo取样算法。我们显示,拟议的方法的组合比所有竞争者都要好得多。我们把拟议的方法应用于人类连接项目(HCP)数据集,我们确定了认知功能的重要的大脑区域和节点特征,并确定了与CO数相关测试的低度测试等级结构。