This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high dimensional brain network into low-dimensional pre-specified features prior to applying standard predictive algorithms. These methods are sensitive to feature choice and inevitably discard important information. Instead, we propose a nonparametric Bayes class of models that utilize the entire adjacency matrix defining brain region connections to adaptively detect predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson-Dirichlet processes to find a lower-dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a "small n, small q" problem, facilitating an effective stochastic search of the predictors. A spike-and-slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are found to outperform existing approaches and applied to a creative reasoning data set.
翻译:本文侧重于基于网络价值预测器预测响应变量的预测问题。 我们的动机是在个人大脑连接网络(连接网)的基础上开发可解释和准确的认知特征和神经-心理紊乱的预测模型。 当前的方法在应用标准预测算法之前将复杂、高维的大脑网络降低到低维的预指定的特征。 这些方法对特性选择十分敏感, 并不可避免地丢弃重要信息 。 相反, 我们提议使用非参数类模型, 利用整个匹配矩阵, 界定大脑区域与适应性检测预测算法的联系, 同时保持可解释性。 Bayesian Connomics (Bacon) 模型类使用Poisson- Dirichlet 进程, 在应用标准预测算法之前, 将复杂、 高维度的大脑网络降低到低维度的预设特征。 小型、 大型问题变成了“ 小的模型 q” 问题, 便利对预测器进行有效的随机搜索。 在分组预测器之前, 峰值和拉布之前, 群集预测师 模型预测师 将使用一种平衡性推算法 。 在模型和 分析中, 我们的后算法中, 将得出后算法 将得出后算法 。