A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent f MRI 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. A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent f MRI 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. 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 that 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. Thus thus for more considerations on parametric settings for interpr eting DMN data.