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

doi:10.1016/j.neuroimage.2020.116866

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Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

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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, …

NeuroImage (Orlando, Fla.), Vol.217, pp.116866-116866

08/15/2020

DOI: 10.1016/j.neuroimage.2020.116866

PMID: 32325210

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Abstract

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. [Display omitted] •Single-band fMRI motion traces show factitious high-frequency content (HF-motion).•The magnitude of HF-motion relates to age and other demographic factors.•HF-motion elevates framewise displacement (FD) and causes data loss.•Substantial fMRI data can be recovered from censoring by filtering motion traces.•Filtering motion traces reduces motion artifacts in functional connectivity.

Functional connectivity

Aging

fMRI

Motion

Artifacts

Details

Title: Subtitle: Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity
Creators: Caterina Gratton - Department of Psychology, Northwestern University, Evanston, IL, 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
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
Tania S Kong - Department of Psychology, 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
Monica Fabiani - Department of Psychology, 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
Daniel Tranel - Department of Neurology, 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
Nico U.F Dosenbach - Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
Abraham Z Snyder - Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
Joel S Perlmutter - Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
Steven E Petersen - Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
Meghan C Campbell - Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
Resource Type: Journal article
Publication Details: NeuroImage (Orlando, Fla.), Vol.217, pp.116866-116866
DOI: 10.1016/j.neuroimage.2020.116866
PMID: 32325210
NLM abbreviation: Neuroimage
ISSN: 1053-8119
eISSN: 1095-9572
Publisher: Elsevier Inc
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health
Language: English
Date published: 08/15/2020
Academic Unit: Neurology; Psychological and Brain Sciences; Iowa Neuroscience Institute
Record Identifier: 9984070848602771

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