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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article number116866
Pages (from-to)116866
JournalNeuroImage
Volume217
DOIs
StatePublished - Aug 15 2020
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank Dr. Hongyu An for helpful discussion of fMRI sequence parameters. Funding was provided by NIH grants MH118370 (CG), F32NS092290 (CG), NS075321 (JSP), NS097437 (MCC), NS058714 (JSP), NS098577-01 (AZS), MH066031 (DMB), AG059878 (MF, GG), R56MH097973 (GG; MF), R25MH112473 (TOL), MH096773 (DAF), MH091238 (DAF), MH115357 (DAF), DA041148 (DAF), MH122066 (DAF; NUFD), NS088590 (NUFD), TR000448 (NUFD), AG063930 (GSW), as well as a McDonnell Foundation Collaborative Activity Award (SEP, DT), a James S. McDonnel Foundation Understanding Human Cognition Award (GSW), pilot funding support from the Northwestern Alzheimer’s Disease Center (NIA P30AG13854 to CG), an award from the Gates Foundation (DAF), the Destafano Innovation Fund (DAF), the Jacobs Foundation (NUFD), an OHSU Fellowship for Diversity and Inclusion in Research Program (OM-D), a Tartar Trust Award (OM-D), the OHSU Parkinson Center Pilot Grant Program (OM-D) and awards from the American Parkinson Disease Association (APDA) Advanced Research Center for PD at WUSTL; Greater St. Louis Chapter of the APDA; McDonnell Center for Systems Neuroscience ; Washington University Institute of Clinical and Translational Sciences (MCC), Barnes Jewish Hospital Foundation (Elliot Stein Family Fund), and the Riney Foundation (JSP). The authors would like to thank Denise Park for providing access to the Dallas Lifespan Brain Study data, collected under NIH grant 5R37AG-006265-25 .

Publisher Copyright:
© 2020 The Authors

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