71 ALTERED VISUOMOTOR PROCESSING IN NA 4 Participants first performed 4 blocks of 8 practice trials at a desktop computer to familiarize themselves with the task. During scanning, the participants lay in a supine position on the scanner bed, with their head fixed in the MRI head coil, a piece of tape attached to their forehead to minimize movement128 and their extended arms resting on the scanner bed, support pillows or their thighs. The stimulus screen was visible through a mirror mounted to the head coil. All images were acquired on a 3T Siemens PrismaFit scanner (Siemens Healthcare, Erlangen, Germany), equipped with a 32-channel head coil. A T1-weighted anatomical scan was acquired with a Magnetization Prepared Rapid Gradient Echo (MPRAGE, TR = 2300 ms, TE = 3.03 ms, TI = 1100 ms, flip angle = 8˚, voxel size = 1.0 x 1.0 x 1.0 mm, slices = 192, FOV = 256 mm, scanning time = 5:21 min). Functional scans were acquired during the task with a multiband 6 sequence (MB6, TR = 1000 ms, TE = 34 ms, acceleration factor = 6, flip angle = 60˚, voxel size = 2.019 x 2.019 x 2.000 mm, slices = 72, FOV = 210 mm, scanning time = 20-30 min dependent on task performance). Preprocessing of neuroimaging data Preprocessing was performed with FSL version 5.0.11 (FMRIB’s Software Library, Oxford, UK). 129 We first removed non-brain structures from the structural image using the Brain Extraction Tool (BET2). 130 Functional images were realigned with fMRI Expert Analysis Tool (FEAT). 131 FEAT additionally applied smoothing (Full Width at Half Maximum (FWHM) = 3 mm) and grand mean scaling and removed non-brain structures from the functional images, which were then registered to the structural image and standard MNI152 space using linear (FLIRT) and non-linear registration (FNIRT). 132-135 Motion-related noise was removed using ICA-AROMA. 136 We manually inspected and, if needed, reclassified137 the 100 independent components that ICA-AROMA generated per participant as noise or signal, after which we applied non-aggressive denoising. Next, we performed nuisance regression on the denoised images, which included regressors of white matter and cerebrospinal fluid, as well as 24 motion parameters. 138 After nuisance regression, we temporally high pass filtered the data at 0.01 Hz and applied additional smoothing with a 5.2 mm FWHM Gaussian kernel, amounting to a final smoothing of 6mm FWHM. See the supplementary materials for a more detailed description of all preprocessing steps. Behavioural analyses Statistical testing was performed using IBM SPSS statistics 25. Unless otherwise specified, statistical tests were two-tailed and alpha-level was set at p = 0.05. We made a comparison between NA patients and healthy participants using a chi square test for sex, and an independent samples t-test for age. Serratus anterior muscle strength of both limbs was compared with a 2-factor mixed ANOVA, which included repeated factor SIDE (left, right) and between-factor GROUP (NA, healthy). To evaluate task performance, we calculated median RTs (on correct trials) and ERs (i.e. number of incorrect trials divided by number of valid (correct + incorrect) trials) for all relevant conditions. Before statistical analyses, ER was normalized through an arcsine transformation. 77 We tested for the effects of between-group factor GROUP (NA, healthy) and repeated factors BIOMECHANICAL COMPLEXITY (easy, complex) and LATERALITY (left, right) on median RT, and normalized ER with two separate 3-factor mixed ANOVAs. We additionally
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