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Moreover, white matter and ventricle reference time courses were created for each participant and added to the general linear model (GLM) along with the six motion correction parameters.
The six motion correction parameters were also included as additional regressors.
In this process, the motion correction parameters were taken as nuisance parameters and were regressed out.
Further nuisance covariates were affine motion correction parameters (R8 13) and global signal of grey matter, white matter and CSF (R14 16).
The 21 images acquired without diffusion weighting at each slice position were used to estimate motion correction parameters using rigid-body transformations [44] implemented in FSL [45].
The motion correction parameters were interpolated for the 180 DW images and combined with a global registration to the T1w anatomy computed with the same method.
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This event allows non-parametric data (e.g., blood pressure, behavioral indices, motion-correction parameters,...) with one value per time point.
For instance, motion-correction parameters typically would not be convolved, but eye traces, which invoke brain activity, presumably would be convolved.
Motion-correction parameters were included in the GLM-Model.
After inspecting the motion-correction parameters we excluded one subject because of movements exceeding 1 mm translation/1° rotation.
Additionally, six covariates corresponding to the motion-correction parameters (rotations and translations) were included.
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