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
Affiliations
Sydney Kaplan
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Corresponding author.
Dominique Meyer
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Oscar Miranda-Dominguez
Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
Anders Perrone
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
Eric Earl
Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
Dimitrios Alexopoulos
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Deanna M. Barch
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
Trevor K.M. Day
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
Joseph Dust
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Adam T. Eggebrecht
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Eric Feczko
Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
Omid Kardan
Department of Psychology, University of Chicago, Chicago, IL, USA
Jeanette K. Kenley
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Cynthia E. Rogers
Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
Muriah D. Wheelock
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Essa Yacoub
Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
Monica Rosenberg
Department of Psychology, University of Chicago, Chicago, IL, USA
Jed T. Elison
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
Damien A. Fair
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
Christopher D. Smyser
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
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.
