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Distinctions Among Real and Apparent Respiratory Motions in Human FMRI Data

Overview
Journal Neuroimage
Specialty Radiology
Date 2019 Jul 26
PMID 31344484
Citations 65
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Abstract

Head motion estimates in functional magnetic resonance imaging (fMRI) scans appear qualitatively different with sub-second image sampling rates compared to the multi-second sampling rates common in the past. Whereas formerly the head appeared still for much of a scan with brief excursions from baseline, the head now appears to be in constant motion, and motion estimates often seem to divulge little information about what is happening in a scan. This constant motion has been attributed to respiratory oscillations that do not alias at faster sampling rates, and investigators are divided on the extent to which such motion is "real" motion or only "apparent" pseudomotion. Some investigators have abandoned the use of motion estimates entirely due to these considerations. Here we investigate the properties of motion in several fMRI datasets sampled at rates between 720 and 1160 ms, and describe 5 distinct kinds of respiratory motion: 1) constant real respiratory motion in the form of head nodding most evident in vertical position and pitch, which can be very large; 2) constant pseudomotion at the same respiratory rate as real motion, occurring only in the phase encode direction; 3) punctate real motions occurring at times of very deep breaths; 4) a low-frequency pseudomotion in only the phase encode direction at and after very deep breaths; 5) slow modulation of vertical and anterior-posterior head position by the respiratory envelope. We reformulate motion estimates in light of these considerations and obtain good concordance between motion estimates, physiologic records, image quality measures, and events evident in the fMRI signals. We demonstrate how variables describing respiration or body habitus separately scale with distinct kinds of head motion. We also note heritable aspects of respiration and motion.

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References
1.
Holmes A, Hollinshead M, OKeefe T, Petrov V, Fariello G, Wald L . Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci Data. 2015; 2:150031. PMC: 4493828. DOI: 10.1038/sdata.2015.31. View

2.
Birn R, Diamond J, Smith M, Bandettini P . Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage. 2006; 31(4):1536-48. DOI: 10.1016/j.neuroimage.2006.02.048. View

3.
Glasser M, Coalson T, Bijsterbosch J, Harrison S, Harms M, Anticevic A . Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. Neuroimage. 2018; 181:692-717. PMC: 6237431. DOI: 10.1016/j.neuroimage.2018.04.076. View

4.
Power J, Plitt M, Gotts S, Kundu P, Voon V, Bandettini P . Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data. Proc Natl Acad Sci U S A. 2018; 115(9):E2105-E2114. PMC: 5834724. DOI: 10.1073/pnas.1720985115. View

5.
Braga R, Buckner R . Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity. Neuron. 2017; 95(2):457-471.e5. PMC: 5519493. DOI: 10.1016/j.neuron.2017.06.038. View