Regular ArticleCharacterization and Correction of Interpolation Effects in the Realignment of fMRI Time Series
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Head motion and physiological effects
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2019, European NeuropsychopharmacologyA Bayesian heteroscedastic GLM with application to fMRI data with motion spikes
2017, NeuroImageCitation Excerpt :For each time point, the corresponding head motion parameters are used to translate and rotate the first volume in the dataset (using interpolation), and the transformed volume is saved as the volume for that specific time point. Even if motion correction is applied to the simulated dataset, the dataset will still contain motion related signal variation (Grootoonk et al., 2000), due to the fact that the interpolation mixes voxels with low and high signal intensity (especially at the edge of the brain, and at the border between different tissue types). It is therefore common to include the estimated head motion parameters in the design matrix, to regress out any motion related variance that remains after the motion correction, and to also account for spin-history artifacts.
Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies
2017, NeuroImageCitation Excerpt :Therefore, the level of motion in rodent fMRI or rs-fMRI experiments is usually acceptable even with the stricter criteria imposed by the higher spatial resolution (Fig. 2). Because the level of motion is typically low and motion correction may introduce artifacts itself (Grootoonk et al., 2000), many animal researchers take the approach of inspecting motion time courses for each scan and discarding any scans where significant motion occurs. In the rodent, respiration can cause apparent motion of the brain due to the movement of the thorax within the magnetic field.
Methods for cleaning the BOLD fMRI signal
2017, NeuroImage