Segregation between the parietal memory network and the default mode network： effects of spatial smoothing and model order in ICA

作     者：Yang Hu Jijun Wang Chunbo Li Yin-Shan Wang Zhi Yang Xi-Nian Zuo Yang Hu;Jijun Wang;Chunbo Li;Yin-Shan Wang;Zhi Yang;Xi-Nian Zuo

作者机构：Key Laboratory of Behavioral Science Laboratory for HumanConnectome and Development Magnetic Resonance ResearchCenter Institute of Psychology Chinese Academy of SciencesBeijing 100101 China University of Chinese Academy of Sciences Beijing 100049China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai 200030 China 

出 版 物：《Science Bulletin》 (科学通报（英文版）)

年 卷 期：2016年第61卷第24期

页      面：1844-1854页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 071006[理学-神经生物学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：supported by the National Basic Research(973)Program(2015CB351702) the National Natural Science Foundation of China(81571756,81270023,81278412,81171409,81000583,81471740,81220108014) Beijing Nova Program(XXJH2015B079 to Z.Y.) the Outstanding Young Investigator Award of Institute of Psychology,Chinese Academy of Sciences(to Z.Y.) the Key Research Program and the Hundred Talents Program of the Chinese Academy of Sciences(KSZD-EW-TZ-002 to X.N.Z) 

主　　题：Default mode network Parietal memory network Independent component analysis Model order Resting-state fMRI Spatial smoothing 

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.