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Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0.1 Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged.

Differences in the blood oxygen level dependent (BOLD) signal changes were studied during voluntary hyperventilation (HV) between young healthy volunteer groups, (1) with intermittent rhythmic delta activity (IRDA) (N = 4) and (2) controls (N = 4) with only diffuse arrhythmic slowing in EEG (normal response). Subjects hyperventilated (3 min) during an 8-min functional MRI in a 1.5-T scanner, with simultaneous recording of EEG (successful with N = 3 in both groups) and physiological parameters. IRDA power and average BOLD signal intensities (of selected brain regions) were calculated. Hypocapnia showed a tendency to be slightly lighter in the controls than in the IRDA group. IRDA power increased during the last minute of HV and ended 10–15 s after HV. The BOLD signal decreased in white and gray matter after the onset of HV and returned to the baseline within 2 min after HV. The BOLD signal in gray matter decreased approximately 30% more in subjects with IRDA than in controls, during the first 2 min of HV. This difference disappeared (in three subjects out of four) during IRDA in EEG. BOLD signal changes seem to depict changes, which precede IRDA. IRDA due to HV in healthy volunteers represent a model with a clearly defined EEG pattern and an observable BOLD signal change.

Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing verified that 60 of the 70 computed components were robustly detectable. Forty-two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other noninterest sources (non-RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non-RSN sources have significantly lower temporal intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details.