Correlation matrices are widely used to analyze the interdependence of variables in various real-world scenarios. Often, a perturbation in a few variables leads to mild differences in many correlation coefficients associated with these variables. We propose an efficient low-dimensional model that characterizes these differences as a product of single-variable effects. We develop methods for point estimation, confidence intervals, and hypothesis testing for this model. Importantly, our methods can account for both the variability in individual correlation matrices and for within-group variability. In simulations, our model shows increased power compared to competing approaches. We use the model to analyze resting-state functional MRI correlation matrices in patients with transient global amnesia and healthy controls. Our model detects significant decreases in synchronization for the patient population in several brain regions, which could not have been detected using previous methods without prior knowledge. Our methods are available in the open-source package \emph{github.com/itamarfaran/corrpops}.
翻译:关联矩阵被广泛用于分析各种现实世界情景中变量的相互依存性。 通常, 几个变量的扰动导致与这些变量相关的许多相关系数的微小差异。 我们提出了一个高效的低维模型, 将这些差异定性为单一可变效应的产物。 我们为这一模型开发了点估、 信任间隔和假设测试方法。 重要的是, 我们的方法可以解释单个相关矩阵的变异性以及群体内部变异性。 在模拟中, 我们的模型显示与竞争方法相比的功率更大。 我们使用该模型来分析具有短暂全球失忆症和健康控制的病人的休息状态功能性 MRI 相关矩阵。 我们的模型检测到几个脑区域病人口的同步性显著下降, 而在没有事先了解的情况下, 无法检测到这些疾病。 我们的方法可以在开放源包\emph{github.com/itamarfaran/corrpops} 中找到。</s>