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Sci Bull (Beijing). 2016;61(24):1844-1854. doi: 10.1007/s11434-016-1202-z. Epub 2016 Dec 5.

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

Author information

1
Key Laboratory of Behavioral Science, Laboratory for Human Connectome and Development, Magnetic Resonance Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China ; University of Chinese Academy of Sciences, Beijing, 100049 China.
2
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030 China.
3
Key Laboratory of Behavioral Science, Laboratory for Human Connectome and Development, Magnetic Resonance Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China ; University of Chinese Academy of Sciences, Beijing, 100049 China ; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030 China.
4
Key Laboratory of Behavioral Science, Laboratory for Human Connectome and Development, Magnetic Resonance Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China.

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 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.

KEYWORDS:

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

Conflict of interest statement

The authors declare that they have no conflict of interest.

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