Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations

Sydney Kaplan, Dominique Meyer, Oscar Miranda-Dominguez, Anders Perrone, Eric Earl, Dimitrios Alexopoulos, Deanna M. Barch, Trevor K.M. Day, Joseph Dust, Adam T. Eggebrecht, Eric Feczko, Omid Kardan, Jeanette K. Kenley, Cynthia E. Rogers, Muriah D. Wheelock, Essa Yacoub, Monica Rosenberg, Jed T. Elison, Damien A. Fair, Christopher D. Smyser

Research output: Contribution to journalArticlepeer-review

Abstract

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., “scrubbing”) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8–24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

Original languageEnglish
Article number118838
JournalNeuroImage
Volume247
DOIs
StatePublished - Feb 15 2022

Keywords

  • Neurodevelopment
  • Neuroimaging, infant
  • Respiratory filtering
  • Resting-state fMRI

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