Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

Abstract

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that grey matter masking improved the reliability of connectivity estimates, whereas de-noising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

“…Moreover, it increased the effects of head motion on functional connectivity. This is in line with the results of previous studies: Varikuti et al found that CompCor was disadvantageous for both within or between‐participant reliability [Varikuti et al, ] while Shirer et al found a negative impact of CompCor specifically on test‐retest reliability [Shirer et al, ]. There may be several reasons for this decrease in reliability.…”

“…We also observed that nuisance regression shifts the distribution of age effects to lower values (except for CompCor), but has little effect on the pattern of age effects across connections. It has been suggested previously that reliability of individual connections is most important for the reliability of groupeffects on connectivity [Varikuti et al, 2017]. However, we observed that improved reliability of the age effect was associated with the reliability of each participant's full connectivity matrix (within-participant reliability), but not with the reliability of the individual differences in connectivity estimates in each ROI pair (between-participant reliability).…”

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity‐based single‐subject classification

“…Given that connectivity patterns of all systems differentiated very well between young and old participants, we acknowledge the possibility that the relevant drivers may be of non‐neural origin. In particular, despite of our optimized confound removal [Power et al, ; Satterthwaite et al, ; Varikuti et al, ], we cannot exclude that residual effects related to motion or brain atrophy as well as physiological effects such as macro‐ and microvascular changes and their cumulative impact on hemodynamic signals [D'Esposito et al, ] may have contributed to our findings.…”

Predicting personality from network-based resting-state functional connectivity

“…Schilbach et al 2016; Varikuti et al 2016) of neuroimaging meta-analyses to provide robust, functionally specific ROIs to investigate individual task-free data (Lee et al 2012). These can help to constrain the otherwise vast feature space for statistical learning on resting-state data in a functionally meaningful and anatomically specific manner (Wang et al 2010).…”

“…This procedure was done separately for men and women ( Sample 1 : 5 males, 5 females; Sample 2 : 4 males, 4 females). No subjects were excluded due to outlier motion parameters (DVARS and FD both displaying zero-centered values) (Salimi-Khorshidi et al 2014; Varikuti et al 2016; Ciric et al 2017). For RSFC analyses, the subject-specific time series for each node of each network were computed as the first eigenvariate of the activity time courses of all gray-matter voxels within 6 mm of the respective peak coordinate.…”