Wang Lu, Zhang Zhengwu, Dunson David
Department of Statistics, Central South University, Changsha 410083, China.
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14604 USA.
IEEE Trans Signal Process. 2019 Apr 1;67(7):1929-1940. doi: 10.1109/tsp.2019.2899818. Epub 2019 Feb 15.
There is an increasing interest in learning a set of small outcome-relevant subgraphs in network-predictor regression. The extracted signal subgraphs can greatly improve the interpretation of the association between the network predictor and the response. In brain connectomics, the brain network for an individual corresponds to a set of interconnections among brain regions and there is a strong interest in linking the brain connectome to human cognitive traits. Modern neuroimaging technology allows a very fine segmentation of the brain, producing very large structural brain networks. Therefore, accurate and efficient methods for identifying a set of small predictive subgraphs become crucial, leading to discovery of key interconnected brain regions related to the trait and important insights on the mechanism of variation in human cognitive traits. We propose a symmetric bilinear model with penalty to search for small clique subgraphs that contain useful information about the response. A coordinate descent algorithm is developed to estimate the model where we derive analytical solutions for a sequence of conditional convex optimizations. Application of this method on human connectome and language comprehension data shows interesting discovery of relevant interconnections among several small sets of brain regions and better predictive performance than competitors.
在网络预测回归中，人们对学习一组与结果相关的小子图越来越感兴趣。提取的信号子图可以极大地改善对网络预测器与响应之间关联的解释。在脑连接组学中，个体的脑网络对应于一组脑区之间的互连，并且人们对将脑连接组与人类认知特征联系起来有着浓厚的兴趣。现代神经成像技术允许对大脑进行非常精细的分割，从而产生非常大的结构性脑网络。因此，准确而高效地识别一组小的预测性子图的方法变得至关重要，这有助于发现与该特征相关的关键互连脑区，并深入了解人类认知特征变异的机制。我们提出了一种带有惩罚项的对称双线性模型，以搜索包含有关响应的有用信息的小团簇子图。开发了一种坐标下降算法来估计模型，在此过程中我们为一系列条件凸优化推导了解析解。将该方法应用于人类连接组和语言理解数据，显示出有趣的发现，即几小组成脑区之间存在相关互连，并且预测性能优于竞争对手。
