PT  - JOURNAL ARTICLE
AU  - Yang Hu
AU  - Jijun Wang
AU  - Chunbo Li
AU  - Yin-shan Wang
AU  - Zhi Yang
AU  - Xi-Nian Zuo
TI  - Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA
AID  - 10.1101/086454
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 086454
4099  - http://biorxiv.org/content/early/2016/11/09/086454.short
4100  - http://biorxiv.org/content/early/2016/11/09/086454.full
AB  - 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 of extracting 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-order model across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.