Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model

• Type of Material: Article
• Authors: Venkatagiri Krishnamurthy, Emory University; Lisa C. Krishnamurthy, Georgia State University; Dina M. Schwam, Georgia State University; Ashley Ealey, Agnes Scott College; Jaemin Shin, Georgia State University; Daphne Greenberg, Georgia State University; Robin D. Morris, Georgia State University
• Language: English
• Date: 2018-03-01
• Publisher: Mary Ann Liebert, Inc.
• Publication Version: Final Published Version
• Copyright Statement: © Mary Ann Liebert, Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1089/brain.2017.0513
• Title of Journal or Parent Work: Brain Connectivity
• Volume: 8
• Issue: 2
• Start Page: 94
• End Page: 105
• Grant/Funding Information: None declared
• Keywords: Neurosciences; Reduction; rsFC MRI; resting state; functional connectivity; MRI; Artifacts; Science & Technology; physiological noise correction; Reliability; Sensory stimulation; Life Sciences & Biomedicine; Neurosciences & Neurology; reading network; language network; Fluctuations; Variance; Human brain; FMRI; Bold signal
• Research Categories: Psychology, Psychobiology; Biology, Neuroscience; Biophysics, Medical

Abstract

It is well accepted that physiological noise (PN) obscures the detection of neural fluctuations in resting-state functional connectivity (rsFC) magnetic resonance imaging. However, a clear consensus for an optimal PN correction (PNC) methodology and how it can impact the rsFC signal characteristics is still lacking. In this study, we probe the impact of three PNC methods: RETROICOR: (Glover et al., 2000), ANATICOR: (Jo et al., 2010), and RVTMBPM: (Bianciardi et al., 2009). Using a reading network model, we systematically explore the effects of PNC optimization on sensitivity, specificity, and reproducibility of rsFC signals. In terms of specificity, ANATICOR was found to be effective in removing local white matter (WM) fluctuations and also resulted in aggressive removal of expected cortical-to-subcortical functional connections. The ability of RETROICOR to remove PN was equivalent to removal of simulated random PN such that it artificially inflated the connection strength, thereby decreasing sensitivity. RVTMBPM maintained specificity and sensitivity by balanced removal of vasodilatory PN and local WM nuisance edges. Another aspect of this work was exploring the effects of PNC on identifying reading group differences. Most PNC methods accounted for between-subject PN variability resulting in reduced intersession reproducibility. This effect facilitated the detection of the most consistent group differences. RVTMBPM was most effective in detecting significant group differences due to its inherent sensitivity to removing spatially structured and temporally repeating PN arising from dense vasculature. Finally, results suggest that combining all three PNC resulted in "overcorrection" by removing signal along with noise.