Anticorrelations in resting state networks without global signal regression

Abstract

Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.

Fig. 1

Illustration of the data analysis methods. The two preprocessing methods are shown in the boxes.

Fig. 2

Connectivity maps (left panel) and correlations values distribution (right panel) after each preprocessing step.

Fig. 3

Functional connectivity maps from the MPFC seed across all participants. a) whole brain regression b) aCompCor, regressing out 5 principal components of the noise ROIs signal.

Fig. 4

Regions of interest used in the comparison of different analysis methods. Yellow: positively-correlated ROIs. Blue: anticorrelated ROI.

Fig. 5

Connectivity from the MPFC seed to the positively correlated ROIs (top left panel), anticorrelated ROIs (bottom panel), and functional related reference ROIs (top right panel). Motion-reg: motion regression, without global regression or aCompCor. WB-reg: whole brain regression. PCA1 – PCA10: aCompCor processing streams after regressing out 1 – 10 principal components of noise ROI signal. Bars represent the mean of the group. Error bars are standard errors.

Fig. 6

Specificity for positively correlated (top) and anticorrelated (bottom) ROIs. Motion-reg: motion regression, without global regression or aCompCor. WB-reg: whole brain regression. PCA1 – PCA10: aCompCor processing streams after regressing out 1 – 10 principal components of noise ROI signal. Error bars are standard errors.