中文摘要：

由二个关键顶骨节点， precuneus 和以后的 cingulate 外皮组成的一个大脑网络，从最近的 fMRI 研究出现了。尽管它解剖地邻近并且空间地与缺省模式网络(DMN ) 重叠，它的函数与存储器处理被联系了，并且它被叫作顶骨存储器网络(PMN ) 。独立部件分析(集成通信适配器) 是过去常同时提取 PMN 和 DMN 的最普通的数据驱动的方法。然而，数据预处理的效果和在 PMN-DMN 分离上的集成通信适配器的参数决心是完全未知的。这里，我们采用组集成通信适配器的三个典型算法估计怎么空间变光滑和模型顺序影响 PMN-DMN 的度分离。我们的调查结果显示那个 PMN 和 DMN 罐头稳定地仅仅用越过三个集成通信适配器算法的低级空间变光滑和高级模型顺序的联合被分开。我们因此为解释 DMN 数据在参量的设置上为更多的考虑争论。

英文摘要：

Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.