Measuring the Influence of Physiological Noise Corrections on ICA Derived Intrinsic Connectivity Brain Networks in Rest and Task fMRI

Physiological noise corrections using RETROICOR algorithm has been shown to increase signal sensitivity in resting state networks such as the default-mode network. However, independent component analysis (ICA)-based network approach may suffer from such corrections especially if there is any overlap between two sources in the decomposition domain. To address the extent the physiological noise corrections may impact ICA derived intrinsic connectivity brain networks, we measured network features including functional network connectivity (FNC), power spectra, and network spatial maps in the resting state and task functional magnetic resonance imaging (fMRI) data that were acquired in the same visit from a group of healthy volunteers. Statistical analysis showed functional connectivity between several networks were significantly changed after RETROICOR corrections in both rest and task fMRI. Significant FNC alterations were found in the subcortical, basal ganglia, salience, and default-mode networks. Power spectra analysis showed a trend toward lower power spectra in the subcortical and salience networks at [0.20 and 0.24] Hz after RETROICOR corrections in both rest and task fMRI. Furthermore, physiological noise corrections led to volumetric decrease in the resting state networks that included the subcortical, basal ganglia, salience, and default-mode networks, and volumetric enlargement in the sensorimotor and cerebellar networks. In task fMRI data, physiological noise corrections generally resulted in the expansion of networks except for task-activated networks including the anterior salience, central executive, dorsal attention, and cerebellar networks. If confirmed with larger sample sizes, these results suggest that physiological noise corrections alter some network features, and that such alterations are different between resting state and task fMRI data.