Anticorrelations in resting state networks without global signal regression

Neuroimage. 2012 Jan 16;59(2):1420-8. doi: 10.1016/j.neuroimage.2011.08.048. Epub 2011 Aug 26.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Brain / physiology*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Male
  • Models, Neurological
  • Models, Statistical
  • Nerve Net / physiology*
  • Pattern Recognition, Automated / methods*
  • Regression Analysis
  • Reproducibility of Results
  • Rest / physiology*
  • Sensitivity and Specificity
  • Statistics as Topic