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Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses.

Catherine E Davey1,2, David B Grayden1, Leigh A Johnston1,2

  • 1Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.

Frontiers in Neuroscience
|March 15, 2021
PubMed
Summary

Functional MRI (fMRI) data exhibits slice-dependent non-stationarities that impact connectivity analysis. A novel correction method restores signal stationarity and improves functional connectivity estimates in BOLD datasets.

Keywords:
connectivitycorrelationfMRInon-stationaritypowerresting-state

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Signal Processing

Background:

  • fMRI BOLD datasets can exhibit slice-dependent non-stationarities.
  • Time-varying signal power during BOLD data acquisition can affect connectivity estimates.
  • Existing methods often assume stationarity, potentially leading to inaccurate results.

Purpose of the Study:

  • To model and address slice-dependent non-stationarities in fMRI BOLD datasets.
  • To analytically derive the impact of non-stationary signal power on functional connectivity.
  • To propose and validate a correction method for slice-dependent non-stationarity.

Main Methods:

  • Development of a model for slice-dependent, non-stationary signal power in fMRI.
  • Analytical derivation of the impact of non-stationarity on pairwise connectivity and correlation variance.
  • Proposal and analytical validation of a correction for slice-dependent non-stationarity.
  • Empirical validation of the correction method using fMRI data.
  • Experimental characterization of non-stationary variance using an inverse Gamma distribution.

Main Results:

  • Non-stationary signal power scales connectivity estimates and increases correlation variance, inducing spurious connectivity.
  • Time-varying power can diminish connectivity estimates.
  • The proposed correction analytically restores signal stationarity and connectivity integrity.
  • Slice-dependent non-stationary variance is optimally characterized by an inverse Gamma distribution.
  • Voxel signal intensity follows a generalized Student's-t distribution, challenging Gaussianity assumptions.

Conclusions:

  • Slice-dependent non-stationarities are a critical factor in fMRI BOLD data affecting connectivity.
  • The proposed correction effectively mitigates the impact of non-stationarity, enhancing connectivity analysis.
  • The findings necessitate a re-evaluation of assumptions in fMRI connectivity methods, particularly regarding signal stationarity and distribution.