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Potential pitfalls when denoising resting state fMRI data using nuisance regression.

Molly G Bright1, Christopher R Tench2, Kevin Murphy3

  • 1Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.

Neuroimage
|December 28, 2016
PubMed
Summary
This summary is machine-generated.

Nuisance regression in resting-state fMRI requires careful statistical modeling. Applying pre-whitening, temporal filtering, and optimized temporal shifting improves the accuracy of cleaned fMRI time-series for better brain fluctuation analysis.

Keywords:
ConnectivityNoise correctionNuisance regressionResting statefMRI

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Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)
  • Statistical Modeling

Background:

  • Resting-state fMRI (rs-fMRI) analysis relies on accurate removal of noise variance.
  • Nuisance regression using the General Linear Model (GLM) is a common denoising technique.
  • The statistical assumptions of GLM in rs-fMRI nuisance regression require scrutiny.

Purpose of the Study:

  • To examine the statistical assumptions of GLM for rs-fMRI nuisance regression.
  • To investigate the impact of pre-whitening, temporal filtering, and temporal shifting on model fit.
  • To provide recommendations for optimizing nuisance regression in rs-fMRI analysis.

Main Methods:

  • Analysis of General Linear Model (GLM) assumptions in the context of fMRI denoising.
  • Use of simulated (toy) and real fMRI data to assess the effects of denoising parameters.
  • Evaluation of pre-whitening, temporal filtering, and temporal shifting of regressors.

Main Results:

  • Pre-whitening is essential for valid statistical inference of noise model fit parameters.
  • Temporal filtering within the noise model improves the accounting for degrees of freedom.
  • Temporal shifting of regressors should be optimized and validated, ideally using a single shift.

Conclusions:

  • Implementing recommended changes to fMRI denoising pipelines can enhance accuracy.
  • Regular assessment of the appropriateness of the noise model is crucial for reliable results.
  • Clear reporting of nuisance regression details in manuscripts will advance the field.