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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Gratton C,Dworetsky A,Coalson RS,Adeyemo B,Laumann TO,Wig GS,Kong TS,Gratton G,Fabiani M,Barch DM,Tranel D,Miranda-Dominguez O,Fair DA,Dosenbach NUF,Snyder AZ,Perlmutter JS,Petersen SE,Campbell MCdoi
10.1016/j.neuroimage.2020.116866subject
Has Abstractpub_date
2020-08-15 00:00:00pages
116866eissn
1053-8119issn
1095-9572pii
S1053-8119(20)30352-9journal_volume
217pub_type
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