Nonparametric group variable selection with multivariate response for connectome-based modelling of cognitive scores
研究结构连接与认知评分之间的关联，使用多元非参数回归模型，通过分组稀疏性和径向基函数网络进行特征选择，并在人类连接组项目数据上发现与认知功能相关的有趣结果。
Abstract We study association between the structural connectivity and cognitive profiles using a multi-response nonparametric regression model. The cognitive profiles are quantified by seven cognitive test scores, and structural connectivity is represented by nine nodal attributes of connectivity graphs. These nodal centralities together encode different connectivity profiles in the brain network. Nodal attributes may be grouped together for each node, motivating us to introduce group sparsity for feature selection, and radial basis function (RBF)-nets are used to quantify the regression effects. An efficient computation algorithm is developed. Applying our proposed method to Human Connectome Project data, we obtain several interesting findings related to cognitive functioning.