Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA

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Temporal concatenation group ICA (TC-GICA) is a widely used data-driven method to extract common functional brain networks among individuals. TC-GICA concatenates the time series of individual fMRI data and applies dimension reduction and ICA algorithms to decompose the data into group-level components. The default mode network (DMN) estimated using TC-GICA at relatively high model orders (i.e., large numbers of components) is split into multiple components. The split DMNs are topographically different from those estimated using other methods (e.g., seed-based correlation, clustering, graph theoretical analysis, and other ICA methods like gRAICAR and IVA-GL) and are inconsistent with the existing knowledge of DMN. We hypothesize that the "DMN-splitting'' phenomenon reflects the impact of inter-individual variability in data, which is propagated into the ICA decomposition via the data-concatenation step of TC-GICA. By systematically manipulating the amount of variability involved in the temporal concatenation in both simulated and several realistic datasets, we observed that as more variability was involved, the estimated DMN became less similar to the averaged functional connectivity (FC) pattern obtained using seed-based correlation analysis. The performance of the DMN estimation in TC-GICA also exhibited remarkable dependence on the model order settings. Further analyses revealed that the “DMN-splitting” in TC-GICA could be reproduced when involving large variability in the data-concatenation and performing ICA at high model orders. These results were replicated across multiple datasets and various software implementations. When applying ICA approaches that avoid temporal concatenation, such as gRAICAR and IVA-GL, to the same datasets, the estimated group-level DMN was more consistent with the seed-based FC pattern and was more robust to various model order settings. This study calls for caution when applying TC-GICA to datasets expected to have large inter-individual variability, such as pooling different experimental groups of subjects.