Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity
Caterina Gratton,
Ally Dworetsky,
Rebecca S. Coalson,
Babatunde Adeyemo,
Timothy O. Laumann,
Gagan S. Wig,
Tania S. Kong,
Gabriele Gratton,
Monica Fabiani,
Deanna M. Barch,
Daniel Tranel,
Oscar Miranda-Dominguez,
Damien A. Fair,
Nico U.F. Dosenbach,
Abraham Z. Snyder,
Joel S. Perlmutter,
Steven E. Petersen,
Meghan C. Campbell
Affiliations
Caterina Gratton
Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Northwestern University, Evanston, IL, USA; Corresponding author. Department of Psychology, 2029 Sheridan Rd., Evanston, IL, 60208, USA.
Ally Dworetsky
Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
Rebecca S. Coalson
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
Babatunde Adeyemo
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
Timothy O. Laumann
Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
Gagan S. Wig
Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, USA
Tania S. Kong
Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
Gabriele Gratton
Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
Monica Fabiani
Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
Deanna M. Barch
Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
Daniel Tranel
Department of Neurology, University of Iowa, Iowa City, IA, USA; Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
Oscar Miranda-Dominguez
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
Damien A. Fair
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
Nico U.F. Dosenbach
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Abraham Z. Snyder
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
Joel S. Perlmutter
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
Steven E. Petersen
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA; Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
Meghan C. Campbell
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
Denoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (>0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0–2.5 s) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 to 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.
