Objective: Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al., and (2) PICA, proposed by Beckmann et al.. Both methods are based on the assumption of statistical independence of the extracted component maps ("spatial ICA"). Group maps are estimated via ICA on pre-calculated group data sets.
Material and methods: In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load.
Results: At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses
Conclusion: Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.