![]() In other words, if temporal interpolation is performed on an fMRI image, it is very likely that less head motion will be in the image afterwards, and that motion estimates will be reduced. The net effect will be to dampen intensity changes that reflect motion, and, in doing so at many voxels, to alter the shape and/or position of the brain. ![]() ![]() Symmetric changes will occur in a voxel vacated for the one volume by the brain. If the signal at this voxel is interpolated in time, then, at the time of motion, the post-interpolation value will be created in part from low signal at neighboring timepoints and will therefore be lower than the original value. Consider a voxel just beyond the cranium, which is empty space for an entire scan except for a brief head movement into and back out of the voxel (lasting a single volume). It matters when motion is estimated in a processing stream because, on first principles, it can be anticipated that temporal interpolation of voxel signals will change brain shape and position. Relatedly, despiking has been recommended as the first step of image preprocessing by some statistical groups. This order of operations is common in the literature: in issues of Neuroimage between September 2015 and January 2016, of 41 fMRI articles that perform slice time correction, 30/41 (73%) performed slice time correction prior to realignment. Similarly, efforts to find neurobiological bases for motion have used this order of operations. For example, many studies of motion artifact in recent years temporally interpolated images prior to estimating motion. But in many studies motion estimation is preceded by temporal interpolation of images, either in the form of slice time correction or outlier replacement (e.g., voxel signal despiking). Motion can be estimated at any step during fMRI image processing and is sometimes the first step of image analysis. These image-derived realignment estimates, judging from simulated data and optical recordings, are good approximations of the motion that occurs in fMRI data. Motion measures are derived from affine transforms produced by rigid body realignment algorithms, usually as one of the first steps in processing a scan. In nearly all fMRI studies, motion is not explicitly measured but is instead estimated from the fMRI data itself. ![]() The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. įunding: This work was supported by the Intramural Research Program, National Institute of Mental Health/NIH (ZIAMH002920 NCT01031407). The work is made available under the Creative Commons CC0 public domain dedication.ĭata Availability: All relevant data are within the paper and its Supporting Information files. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. Received: Accepted: JPublished: September 7, 2017 PLoS ONE 12(9):Įditor: Xi-Nian Zuo, Institute of Psychology, Chinese Academy of Sciences, CHINA We also find that outlier replacement procedures change signals almost entirely during times of motion and therefore have notable similarities to motion-targeting censoring strategies (which withhold or replace signals entirely during times of motion).Ĭitation: Power JD, Plitt M, Kundu P, Bandettini PA, Martin A (2017) Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection. Based on these findings, it is sensible to obtain motion estimates prior to any image processing (regardless of subsequent processing steps and the actual timing of motion correction procedures, which need not be changed). These reduced motion estimates will be particularly problematic for studies needing estimates of motion in time, such as studies of dynamics. Such reductions also degrade the sensitivity of analyses aimed at detecting motion-related artifact and can cause a dataset with artifact to falsely appear artifact-free. Such reductions make the data seem to be of improved quality. Estimated head motion was reduced by 10–50% or more following temporal interpolation, and reductions were often visible to the naked eye. Here we demonstrate this effect and its consequences in five large fMRI datasets. From first principles it can be anticipated that temporal interpolation will alter head motion in a scan. Processing steps involving temporal interpolation (e.g., slice time correction or outlier replacement) often precede motion estimation in the literature. Head motion can be estimated at any point of fMRI image processing.
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