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Identifying and characterizing systematic temporally-lagged BOLD artifacts.

Lisa Byrge1, Daniel P Kennedy1

  • 1Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington, IN 47405, United States.

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Summary

Residual noise in functional MRI (fMRI) affects findings. We developed a tool to detect lagged BOLD signal changes from motion and respiration, revealing structured noise that impacts functional connectivity.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Biomedical Engineering

Background:

  • Residual noise in Blood-Oxygen-Level-Dependent (BOLD) signals complicates functional magnetic resonance imaging (fMRI) analyses, especially functional connectivity.
  • Noise sources like motion and respiration can introduce temporally lagged effects, spuriously influencing results and individual difference studies.

Purpose of the Study:

  • To develop and validate a tool for assessing residual lagged structure in the BOLD signal associated with nuisance variables.
  • To investigate the impact of motion (framewise displacement) and respiration on the BOLD signal and functional connectivity.

Main Methods:

  • A novel tool, analogous to a peri-event time histogram, was used to assess lagged BOLD signal structure.
  • The method evaluated the relationship between framewise displacement, respiration, and the global cortical signal.
  • Functional connectivity estimates were analyzed before and after applying global signal regression.

Main Results:

  • Framewise displacements, regardless of magnitude, induced prolonged, global BOLD signal changes lasting tens of seconds.
  • This residual lagged structure explained a significant portion (30-40%) of the global cortical signal variance and affected functional connectivity estimates.
  • Respiratory fluctuations showed similar lagged BOLD patterns, suggesting respiration as a contributing factor to motion-linked artifacts.

Conclusions:

  • Caution is needed when interpreting individual differences in fMRI studies due to potential noise covariation.
  • The developed method effectively identifies lagged BOLD structure, highlighting the need for advanced preprocessing techniques to mitigate these artifacts.