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Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group\u2010level fMRI data. However, these group\u2010level spatial patterns (SPs) were mostly based on random\u2010effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group\u2010level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed\u2010effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation\u2010based group independent component (IC) analysis, and DMN\u2010related ICs at the group\u2010level were automatically selected. The individual\u2010level SPs of these DMN\u2010related ICs were subsequently estimated using a dual\u2010regression approach. Using these individual\u2010level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center\u2010of\u2010clusters. The resulting SPs from the MFX\u2010based group inference showed a significantly greater level of reproducibility than those from the RFX\u2010based group inference as tested in a bootstrapping framework Family\u2010wise error (FWE)\u2010corrected <jats:italic>p<\/jats:italic> &lt; 10<jats:sup>\u221210<\/jats:sup>, one\u2010way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group\u2010 or condition\u2010dependent characteristic traits implicated in DMNs. \u00a9 2012 Wiley Periodicals, Inc. 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