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

doi:10.1016/j.neuroimage.2021.118838

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

Neuroimage. 2022 Feb 15:247:118838. doi: 10.1016/j.neuroimage.2021.118838. Epub 2021 Dec 20.

Authors

Sydney Kaplan¹, Dominique Meyer², Oscar Miranda-Dominguez³, Anders Perrone⁴, Eric Earl⁵, Dimitrios Alexopoulos², Deanna M Barch⁶, Trevor K M Day⁷, Joseph Dust², Adam T Eggebrecht⁸, Eric Feczko³, Omid Kardan⁹, Jeanette K Kenley², Cynthia E Rogers¹⁰, Muriah D Wheelock⁸, Essa Yacoub¹¹, Monica Rosenberg⁹, Jed T Elison¹², Damien A Fair¹³, Christopher D Smyser¹⁴

Affiliations

¹ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA. Electronic address: sydney.kaplan@wustl.edu.
² Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
³ Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
⁴ Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA.
⁵ Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA.
⁶ 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.
⁷ Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
⁸ Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
⁹ Department of Psychology, University of Chicago, Chicago, IL, USA.
¹⁰ Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹¹ Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
¹² 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.
¹³ 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.
¹⁴ 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.

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.

Keywords: Neurodevelopment; Neuroimaging, infant; Respiratory filtering; Resting-state fMRI.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Artifacts
Connectome / methods
Female
Head Movements
Humans
Image Processing, Computer-Assisted / methods*
Infant
Magnetic Resonance Imaging / methods*
Male
Motion*
Neuroimaging / methods*
Respiration
Sleep

Abstract

Publication types

MeSH terms

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