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Related Experiment Video

Updated: Jan 21, 2026

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Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression.

Alican Nalci1, Wenjing Luo2, Thomas T Liu3

  • 1Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.

Neuroimage
|July 24, 2019
PubMed
Summary
This summary is machine-generated.

Nuisance factors like head motion significantly impact functional connectivity (FC) estimates in fMRI scans. Even after regression, these factors influence FC variability, requiring careful interpretation of scan-to-scan FC measures.

Keywords:
Global signalNuisance regressionVariabilityfunctional connectivity

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Area of Science:

  • Neuroimaging
  • Functional Connectivity Analysis
  • Brain Signal Processing

Background:

  • Resting-state functional MRI (fMRI) uses blood-oxygen-level-dependent (BOLD) signal correlations to estimate functional connectivity (FC).
  • FC estimates are susceptible to nuisance factors such as scanner artifacts and physiological noise.
  • Nuisance regression is standard practice to mitigate these effects on a per-scan basis.

Purpose of the Study:

  • To investigate the impact of nuisance factors on the variability of FC estimates across multiple fMRI scans.
  • To evaluate the effectiveness of nuisance regression in reducing inter-scan FC variability.
  • To analyze the influence of global signal regression (GSR) on FC estimates.

Main Methods:

  • Analysis of inter-scan variability in FC estimates before and after nuisance regression.
  • Correlation analysis between FC variations and nuisance terms (head motion, white matter, CSF, global signal).
  • Assessment of global signal regression effects on FC estimates.

Main Results:

  • Inter-scan FC variations correlate significantly with nuisance factor norms (head motion, WM, CSF, GS) both before and after regression.
  • Global signal regression can introduce negative correlations between GS norm fluctuations and inter-scan FC.
  • Empirical findings align with theoretical predictions for dynamic FC measures.

Conclusions:

  • Nuisance factors substantially influence FC variability across scans, even after standard regression techniques.
  • Caution is advised when interpreting inter-scan FC measures due to persistent nuisance factor effects.
  • Global signal regression requires careful consideration due to its potential to introduce confounding fluctuations.