Nuisance Effects and the Limitations of Nuisance Regression in Dynamic Functional Connectivity fMRI

Abstract

In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from di↵erent brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the e↵ects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between DFC estimates and nuisance factors.We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors, with significant correlations observed in the majority (76%) of the cases examined. Significant correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed in the majority (71%) of the cases examined after nuisance regression. We present theoretical bounds on the di↵erence between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the ⇤ Corresponding Author ⇤⇤ Principal Corresponding Author Email addresses: analci@ucsd.edu (Alican Nalci), ttliu@ucsd.edu (Thomas T. Liu). CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/285239 doi: bioRxiv preprint first posted online Mar. 21, 2018; e cacy of nuisance regression with regards to DFC estimates.

Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression

“…Our work significantly extends the preliminary results regarding static FC estimates presented in Nalci et al (2019). We provide a more extensive analysis of various nuisance effects on the FC estimates before and after NR.…”

Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

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Dynamical differential covariance recovers directional network structure in multiscale neural systems

“…To assess the statistical significance of the estimated connection, we used an autoregressive (AR) bootstrap procedure (40,41) to preserve the power spectrum density (PSD) of BOLD signals. For a specific estimated connection, denoted as element (i, j), our null hypothesis was that signal x i and x j are independent regardless of other nodes' influence.…”

“…bootstrap procedure [5,12] to preserve the power spectrum density (PSD) of BOLD signals. For a specific estimated connection, denoted as element (i, j), our null hypothesis was that signal x i and…”