Detecting associations between intact connectomes and clinical covariates using recursive partitioning object-oriented data analysis.
Detecting associations between intact connectomes and clinical covariates using recursive partitioning object-oriented data analysis.
复制标题
使用递归分区面向对象的数据分析来检测完整连接组和临床协变量之间的关联。
DOI：
10.1002/sim.8374
复制
发表时间：
2019
影响因子：
2
通讯作者：
Shannon,William
中科院分区：
文献类型：
--
作者：
Yang,Dake;Deych,Elena;Shands,Berkley;Campbell,MeghanC;Perlmutter,JoelS;Petersen,Steve;Schlaggar,BradleyL;Shannon,William
许多神经科学家对连接组（大脑区域之间功能连接的图形表示）如何随协变量变化感兴趣。在统计学中，像这样的变化是用回归分析的，其中结果或因变量回归到协变量上。然而，当结果是一个复杂的对象，如连接图，经典的回归模型不能使用。这里开发的回归方法与复杂的图形结果结合递归分区与吉布斯分布。我们将只讨论连接体的应用，但该方法通常适用于任何图形结果。该方法称为Gibbs‐RPart，将协变量空间划分为一组不重叠的区域，使得区域内的连接体比其他区域中的连接体更相似。本文扩展了基于吉布斯分布的图值数据的面向对象数据分析范式，我们以前曾将其应用于假设检验，以比较来自不同群体的连接体群体（参见La Rosa等人的工作）。
Many neuroscientists are interested in how connectomes (graphical representations of functional connectivity between areas of the brain) change in relation to covariates. In statistics, changes like this are analyzed using regression, where the outcomes or dependent variables are regressed onto the covariates. However, when the outcome is a complex object, such as connectome graphs, classical regression models cannot be used.The regression approach developed here to work with complex graph outcomes combines recursive partitioning with the Gibbs distribution. We will only discuss the application to connectomes, but the method is generally applicable to any graphical outcome. The method, called Gibbs‐RPart, partitions the covariate space into a set of nonoverlapping regions such that the connectomes within regions are more similar than they are to the connectomes in other regions. This paper extends the object‐oriented data analysis paradigm for graph‐valued data based on the Gibbs distribution, which we have applied previously to hypothesis testing to compare populations of connectomes from distinct groups (see the work of La Rosa et al).