Group inference of default-mode networks from functional magnetic resonance imaging data: Comparison of random- and mixed-effects group statistics

Default-mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group-level fMRI data. However, these group-level spatial patterns (SPs) were mostly based on random-effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group-level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed-effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation-based group independent component (IC) analysis, and DMN-related ICs at the group-level were automatically selected. The individual-level SPs of these DMN-related ICs were subsequently estimated using a dual-regression approach. Using these individual-level 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-of-clusters. The resulting SPs from the MFX-based group inference showed a significantly greater level of reproducibility than those from the RFX-based group inference as tested in a bootstrapping framework Family-wise error (FWE)-corrected p < 10 ^-10, one-way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group- or condition-dependent characteristic traits implicated in DMNs.