Network anticorrelations, global regression, and phase‐shifted soft tissue correction

Abstract

Synchronized low-frequency BOLD fluctuations are observed in dissociable large-scale, distributed networks with functional specialization. Two such networks, referred to as the task-positive network (TPN) and the task-negative network (TNN) because they tend to be active or inactive during cognitively demanding tasks, show reproducible anticorrelation of resting BOLD fluctuations after removal of the global brain signal. Because global signal regression mandates that anticorrelated regions to a given seed region must exist, it is unclear whether such anticorrelations are an artifact of global regression or an intrinsic property of neural activity. In this study, we demonstrate from simulated data that spurious anticorrelations are introduced during global regression for any two networks as a linear function of their size. Using actual resting state data, we also show that both the TPN and TNN become anticorrelated with the orbits when soft tissues are included in the global regression algorithm. Finally, we propose a technique using phase-shifted soft tissue regression (PSTCor) that allows improved correction of global physiological artifacts without global regression that shows improved anatomic specificity to global regression but does not show significant network anticorrelations. These results imply that observed anticorrelations between TNN and TPN may be largely or entirely artifactual in the resting state. These results also imply that differences in network anticorrelations attributed to pathophysiological or behavioral states may be due to differences in network size or recruitment rather than actual anticorrelations.

The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

“…In addition, these techniques were compared against two of the most widely used noise correction strategies that use nuisance regressors derived from the data itself-the regression of average white matter and CSF signals, and the regression of these signals as well as the average whole-brain signal. There are, of course, a large number of additional physiological noise correction techniques [e.g., CompCor (Behzadi et al, 2007), PESTICA (Beall and Lowe, 2007), CORSICA (Perlbarg et al, 2007), PSTCor (Anderson et al, 2011), and FIX (Griffanti et al, 2014)] that do not use independent measures of physiology but primarily estimate noise contributions from the data itself. Furthermore, future studies could also examine the influence of dynamic B0-field corrections at reducing physiological noise (Roopchansingh et al, 2003).…”

Topographic maps of multisensory attention

“…Resting BOLD fMRI data were obtained from 58 normal, healthy adolescent and adult volunteers, examined after informed consent in accordance with procedures approved by the University of Utah Institutional Review Board (mean age 18.0 ± 4.9 y, age range 11-35, 32 male, 26 female.) A subset of this data has been previously reported (26). All subjects had no Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV axis I diagnoses on the basis of diagnostic semistructured psychiatric interview.…”

“…Postprocessed time series data from each voxel of the brain in each subject were compared with time series averaged from voxels within a 5-mm radius seed in the posterior cingulate (MNI: x = −5, y = −52, z = 40) and right IPS (MNI: x = 50, y = −41, z = 52) regions using Pearson correlation coefficients to identify the default mode and attention control networks, respectively (26). The correlation values for each subject were Fisher transformed and a second level analysis was performed in SPM8, with threshold of T > 12 and T > 8 used for high and low thresholds for significantly correlated voxels in each network.…”

Save the Global: Global Signal Connectivity as a Tool for Studying Clinical Populations with Functional Magnetic Resonance Imaging

“…However, it has also been claimed that regression of the global signal does not remove breathing artifacts from functional data and may introduce artificial anti-correlations (Anderson et al, 2011;Carbonell et al, 2011;Murphy et al, 2009;Weissenbacher et al, 2009; but also see Chang and Glover, 2009;Chai et al 2012). Furthermore, it has recently been shown that global signal removal decreases 1 year test-retest reliability of seed-based FC measures (Guo et al, 2012).…”