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

36 Scopus citations


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
Article number116866
StatePublished - Aug 15 2020


  • Aging
  • Artifacts
  • Functional connectivity
  • Motion
  • fMRI


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