Elsevier

NeuroImage

Volume 59, Issue 2, 16 January 2012, Pages 1420-1428
NeuroImage

Anticorrelations in resting state networks without global signal regression

https://doi.org/10.1016/j.neuroimage.2011.08.048Get rights and content

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.

Research highlights

►Resting-state connectivity analysis without global signal regression. ►Principal components from noise regions-of-interest were regressed out (CompCor). ►Robust anticorrelations were observed under CompCor. ►Specificity was equal or higher under CompCor compared to global signal regression.

Introduction

Coherent low frequency fluctuations in the resting state of the blood oxygenation level-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) are thought to reflect the intrinsic organization of the brain [see (Buckner et al., 2008, Fox and Raichle, 2007) for review]. Resting-state fMRI has revealed that signals in functionally related brain regions correlate with each other even in the absence of external stimuli (Beckmann et al., 2005, Biswal et al., 1995, De Luca et al., 2006, Fox et al., 2005, Fransson, 2005, Greicius et al., 2003). Functional networks identified by resting-state fMRI have been shown to be robust and reliable (Damoiseaux et al., 2006, Shehzad et al., 2009, Van Dijk et al., 2010, Zuo et al., 2010), and can thus provide useful information about brain organization differences across different clinical populations (Dosenbach et al., 2010, Seeley et al., 2009) and during development (Dosenbach et al., 2010).

It has been proposed that some systems in the brain are intrinsically organized into anticorrelated networks in resting-state fMRI. Specifically, the default mode network, which consists of a set of brain regions that are commonly deactivated during tasks that demand external attention, has been found to be anticorrelated with regions of the task-positive network, which consists of a set of regions that are commonly activated in tasks that demand attention and mental control (Fox et al., 2005, Fransson, 2005, Greicius et al., 2003, Kelly et al., 2008, Uddin et al., 2009). The strength of the negative correlation between the default mode network regions and task-positive network regions has been linked to variability in task performance (Hampson et al., 2010, Kelly et al., 2008) and individual differences in task-induced BOLD activity (Mennes et al., 2010). Abnormalities in these two anticorrelated networks have been found in patients with schizophrenia (Whitfield-Gabrieli et al., 2009), ADHD (Castellanos et al., 2008), bipolar disorder (Chai et al., 2011), and Alzheimer's disease (Wang et al., 2007).

However, it remains unclear whether the anticorrelations observed in resting-state fMRI are neurobiologically valid or are instead artificially introduced by global signal regression, a preprocessing technique for removing physiological and other noise in fMRI BOLD time series (Aguirre et al., 1997, Aguirre et al., 1998, Desjardins et al., 2001, Macey et al., 2004, Zarahn et al., 1997). Global signal, the average signal across all voxels in the brain, is removed in some fMRI studies to correct for physiological noise, such as respiratory and cardiac noise, under the assumption that global signal is not correlated with task-induced signal. However, when global signal is influence by experimental manipulations, removing global signal can decrease task-related activation in fMRI studies (Aguirre et al., 1998, Junghofer et al., 2005). In seed-based resting-state fMRI analysis, physiological noise and signal fluctuation caused by residual motion and other artifacts can introduce spurious correlation among brain regions and result in overestimation of connectivity. Global signal is commonly removed using a general linear model (GLM) technique to improve the specificity of functional connectivity analysis (Fox et al., 2005, Van Dijk et al., 2010, Weissenbacher et al., 2009). However, global signal regression shifts the distribution of the correlation values of a seed region toward the negative direction such that they must sum to less than zero (Murphy et al., 2009). It has been suggested that anticorrelations in resting-state connectivity are most likely artificially introduced by global signal regression, calling into question of the functional significance of anticorrelations observed in resting-state connectivity (Murphy et al., 2009). Previous studies have not reached a consistent conclusion on this issue (Chang and Glover, 2009, Fox et al., 2009, Hampson et al., 2010, Van Dijk et al., 2010, Weissenbacher et al., 2009).

In the present study, we examined positive and negative correlations in resting-state connectivity using a component base noise reduction method (CompCor) (Behzadi et al., 2007). The CompCor method corrects for physiological noise by regressing out principal components from noise regions-of-interest (ROI), such as the white matter and cerebral spinal fluid (CSF) regions, in which signal is unlikely to be related to neural activity. Compared to the average signal from white matter and CSF regions, principal components derived from these noise ROIs can better account for voxel-specific phase differences in physiological noise. Applying CompCor to BOLD time series significantly reduced noise from physiological and other sources (Behzadi et al., 2007). Here we compared functional connectivity from a default mode network seed region in the medial prefrontal cortex (MPFC) under two separate preprocessing streams: the CompCor approach that does not remove global signal, and the whole brain regression method in which the global signal was removed. We hypothesized that anticorrelations from the MPFC seed should emerge without global signal regression, when physiological and other spurious noise are effectively removed using the CompCor approach.

Section snippets

Participants

Fifteen healthy participants (mean age: 37.3 ± 2.4, 9 males) were included in the study. All participants were right-handed, had no history of psychiatric or neurological illness as confirmed by a psychiatric clinical assessment. The study was approved by the institutional review boards of McLean Hospital. Signed informed consent was obtained prior to participation.

Imaging procedure

Data were acquired on a 3T Siemens scanner using a standard head coil. T1-weighted whole brain anatomy images (MPRAGE sequence, 256 × 

Results

The distribution of the correlation values before and after whole brain signal regression or aCompCor preprocessing is shown in Fig. 2. Correlation values were predominantly in the positive range before whole brain regression or aCompCor. Whole brain regression shifted the distribution toward the negative range.

Regions positively correlated with the MPFC seed, including the posterior cingulate cortex, left and right lateral parietal cortices, bilateral parahippocampal gyri, bilateral inferior

Discussion

We examined the magnitude and specificity of resting-state connectivity using two different data processing methods, to test the hypothesis that anticorrelation during rest is not artificially introduced by global signal regression. Our results highlight that the global regression can result in artifactual anticorrelations (as found between MPFC and functionally unrelated reference regions). In contrast, the aCompCor approach correctly removes these effects (no associations found between MPFC

Acknowledgments

This research was supported by 5K23MH079982-03 (Dr. Öngür) from the National Institute of Mental Health and by the Poitras Center for Affective Disorders Research at the McGovern Institute for Brain Research at MIT. The authors declare no financial interests or potential conflicts of interest with this work.

References (45)

  • A.E. Desjardins et al.

    Removal of confounding effects of global signal in functional MRI analyses

    Neuroimage

    (2001)
  • M. Hampson et al.

    Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance

    Magn. Reson. Imaging

    (2010)
  • M. Junghofer et al.

    Neuroimaging of emotion: empirical effects of proportional global signal scaling in fMRI data analysis

    Neuroimage

    (2005)
  • A.M. Kelly et al.

    Competition between functional brain networks mediates behavioral variability

    Neuroimage

    (2008)
  • P.M. Macey et al.

    A method for removal of global effects from fMRI time series

    Neuroimage

    (2004)
  • M. Mennes et al.

    Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity

    Neuroimage

    (2010)
  • K. Murphy et al.

    The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

    Neuroimage

    (2009)
  • W.W. Seeley et al.

    Neurodegenerative diseases target large-scale human brain networks

    Neuron

    (2009)
  • A. Weissenbacher et al.

    Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies

    Neuroimage

    (2009)
  • E. Zarahn et al.

    Empirical analyses of BOLD fMRI statistics. I. Spatially unsmoothed data collected under null-hypothesis conditions

    Neuroimage

    (1997)
  • X.N. Zuo et al.

    Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach

    Neuroimage

    (2010)
  • C.F. Beckmann et al.

    Investigations into resting-state connectivity using independent component analysis

    Philos. Trans. R. Soc. Lond. B Biol. Sci.

    (2005)
  • Cited by (791)

    View all citing articles on Scopus
    View full text