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

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• 作者：Yang Hu ; Jijun Wang ; Chunbo Li ; Yin-Shan Wang ; Zhi Yang ; Xi-Nian Zuo
• 关键词：Default mode network ; Parietal memory network ; Independent component analysis ; Model order ; Resting ; state fMRI ; Spatial smoothing
• 刊名：Science Bulletin
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：61
• 期：24
• 页码：1844-1854
• 全文大小：2020KB
• 刊物主题：Science, Humanities and Social Sciences, multidisciplinary; Life Sciences, general; Physics, general; Chemistry/Food Science, general; Earth Sciences, general; Engineering, general;
• 出版者：Science China Press
• ISSN：2095-9281
• 卷排序：61

文摘

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 determination 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.