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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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A new fMRI quality metric using multi-echo information: Theory, validation and implications.

Javier Gonzalez-Castillo1,2, César Caballero-Gaudes2,3, Daniel A Handwerker1

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We introduce p_BOLD, a novel quality assurance metric for multi-echo fMRI data. This metric quantifies the BOLD signal

Keywords:
denoisingglobal signal regressionmulti-echo fMRIquality assurance

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Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)
  • Biophysics

Background:

  • High-quality fMRI data is crucial for reliable results, but is often compromised by confounding factors and noise.
  • Existing quality assurance (QA) metrics for fMRI do not fully utilize the information available in multi-echo (ME) acquisitions.
  • There is a need for novel QA metrics that specifically leverage the unique properties of ME-fMRI data.

Purpose of the Study:

  • To introduce and validate a new QA metric, termed p_BOLD, for ME-fMRI.
  • To quantify the extent to which ME-fMRI signal fluctuations are dominated by Blood Oxygenation Level-Dependent (BOLD) contrast.
  • To assess the utility of p_BOLD in evaluating fMRI preprocessing pipelines and its relationship with other QA metrics like TSNR.

Main Methods:

  • Theoretical principles of the p_BOLD metric were established.
  • p_BOLD efficacy was validated on a small dataset (N=7) with controlled BOLD fluctuation levels.
  • p_BOLD was applied to a large public ME-fMRI dataset (N=439) to compare preprocessing pipelines and its relationship with TSNR.

Main Results:

  • ME-based denoising strategies improved both p_BOLD and TSNR compared to basic denoising.
  • Including the global signal (GS) as a regressor improved TSNR but decreased p_BOLD.
  • The decrease in p_BOLD with GS regression suggests a reduction in neural BOLD fluctuations, not physiological ones.

Conclusions:

  • p_BOLD is a valuable new QA metric for ME-fMRI, offering complementary information to TSNR.
  • The metric can differentiate the impact of various denoising strategies on BOLD signal dominance.
  • Higher p_BOLD values correlate with improved predictive power of phenotypes from functional connectivity data, highlighting its utility in downstream analyses.