An ICA Investigation into the Effect of Physiological Noise Correction on Dynamic Functional Network Connectivity and Meta-state Metrics

Abstract

Physiological fluctuations such as cardiac pulsations (heart rate) and respiratory rhythm (breathing) have been studied in the resting state functional magnetic resonance imaging (rs-fMRI) studies as the potential sources of confounds in functional connectivity. Independent component analysis (ICA) provides a data driven approach to investigate functional connectivity at the network level. However, the effect of physiological noise correction on the dynamic of ICA-derived networks has not yet been studied. The goal of this study was to investigate the effect of retrospective correction of cardiorespiratory artifacts on the time-varying aspects of functional network connectivity. Blood oxygenation-level dependent (BOLD) rs-fMRI data were collected from healthy subjects using a 3.0T MRI scanner. Whole-brain dynamic functional network connectivity (dFNC) was computed using sliding time window correlation, and k-means clustering of windowed correlation matrices. Results showed significant effects of physiological denoising on dFNC between several network pairs in particular the subcortical, and cognitive/attention networks (false discovery rate [FDR]-corrected p < 0.01). Meta-state dynamics further revealed significant changes in the number of unique windows for each subject, number of times each subject changes from one meta-state to other, and sum of L1 distances between successive meta-states. In conclusion, removal of artifacts is important for achieving reliable fMRI results, however a more cautious approach should be adapted in regressing such "noise" in ICA functional connectivity approach. More experiments are needed to investigate impact of denoising on dFNC especially across different datasets.