Title | ||
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Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression |
Abstract | ||
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In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression. |
Year | DOI | Venue |
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2019 | 10.1016/j.neuroimage.2019.07.018 | NeuroImage |
Keywords | Field | DocType |
functional connectivity,Nuisance regression,Global signal,Variability | Regression,Biology,Nuisance,Correlation,Statistics,Genetics | Journal |
Volume | ISSN | Citations |
202 | 1053-8119 | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Alican Nalci | 1 | 48 | 3.23 |
Wenjing Luo | 2 | 1 | 0.34 |
Thomas T Liu | 3 | 1022 | 76.03 |