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Quantitative Assessment Protocol for Facial Soft Tissue Volumetric Changes with Stereophotogrammetry
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Network anticorrelations, global regression, and phase-shifted soft tissue correction.

Jeffrey S Anderson1, T Jason Druzgal, Melissa Lopez-Larson

  • 1Division of Neuroradiology, University of Utah, Salt Lake City, UT 84132, USA. andersonjeffs@gmail.com

Human Brain Mapping
|June 10, 2010
PubMed
Summary

Resting-state anticorrelations between the task-positive network (TPN) and task-negative network (TNN) may be artifacts of global signal regression. A new method, PSTCor, corrects physiological artifacts without introducing spurious network anticorrelations.

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Area of Science:

  • Neuroimaging
  • Resting-state functional magnetic resonance imaging (fMRI)
  • Network neuroscience

Background:

  • Synchronized low-frequency fluctuations in Blood-Oxygen-Level-Dependent (BOLD) signals reveal large-scale brain networks.
  • The task-positive network (TPN) and task-negative network (TNN) exhibit reproducible anticorrelations in resting-state BOLD signals after global signal regression.
  • The origin of these anticorrelations—whether intrinsic neural properties or analysis artifacts—remains debated.

Purpose of the Study:

  • To investigate whether observed anticorrelations between the TPN and TNN are artifacts introduced by global signal regression.
  • To evaluate the impact of global signal regression on network anticorrelations using simulated and real resting-state fMRI data.
  • To introduce and validate a novel method for correcting physiological artifacts without inducing spurious network anticorrelations.

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Last Updated: Jun 12, 2026

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Main Methods:

  • Simulated fMRI data were used to demonstrate how global signal regression can introduce spurious anticorrelations as a function of network size.
  • Resting-state fMRI data were analyzed with and without soft tissue regression to assess its effect on TPN-TNN anticorrelations.
  • A new technique, phase-shifted soft tissue regression (PSTCor), was developed and applied to resting-state data for artifact correction.

Main Results:

  • Simulations confirmed that global signal regression introduces spurious anticorrelations between networks, linearly dependent on their size.
  • In real resting-state data, including soft tissues in global regression led to anticorrelations between the TPN, TNN, and surrounding tissues.
  • PSTCor effectively corrected global physiological artifacts with improved anatomic specificity and eliminated significant network anticorrelations.

Conclusions:

  • Observed resting-state anticorrelations between the TNN and TPN are likely largely or entirely artifactual, stemming from global signal regression.
  • Differences in reported network anticorrelations across studies may be attributable to variations in network size or recruitment, not true neural phenomena.
  • PSTCor offers a promising alternative for artifact correction in resting-state fMRI, preserving genuine neural signals without introducing spurious network interactions.