Computational protocol: Connectomic markers of symptom severity in sport-related concussion: Whole-brain analysis of resting-state fMRI

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Protocol publication

[…] Athletes were imaged at St. Michael's Hospital using a research-dedicated MRI system operating at 3 Tesla (Magnetom Skyra, Siemens, Erlangen, Germany) with the standard 20-channel head receiver coil. Structural imaging included three-dimensional (3D) T1-weighted Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE: inversion time (TI)/echo time (TE)/repetition time (TR) = 1090/3.55/2300 ms, flip angle (FA) = 8°,192 sagittal slices with field of view (FOV) = 240 × 240 mm, 256 × 256 pixel matrix, 0.9 mm slice thickness, 0.9 × 0.9 mm in-plane resolution, with bandwidth (BW) = 200 Hertz per pixel (Hz/px), fluid attenuated inversion recovery imaging (FLAIR: TI/TE/TR = 1800/387/5000 ms, 160 sagittal slices with FOV = 230 × 230 mm, 512 × 512 matrix, 0.9 mm slice thickness, 0.4 × 0.4 mm in-plane resolution, BW=751 Hz/px) and susceptibility-weighted imaging (SWI: TE/TR = 20/28 ms, FA = 15°, 112 axial slices with FOV = 193 × 220 mm, 336 × 384 matrix, 1.2 mm slice thickness, 0.6 × 0.6 mm in-plane resolution, BW=120 Hz/px). Structural images were reviewed in a 2-step procedure, consisting of initial inspection by an MRI technologist during the imaging session and later review by a neuroradiologist with clinical reporting, if any abnormalities were identified. Statistical testing was also performed by obtaining mean, variance and skew of voxel signal intensity distributions for masked MPRAGE, FLAIR and SWI images, generating a Z-score for each imaging sequence per athlete relative to the control distribution and identifying statistically significant outliers at p < 0.05. No abnormalities (white matter hyper-intensities, contusions, micro-hemorrhage, or statistical outliers) were found for the concussed athletes and controls in this study.Resting-state fMRI was acquired via multi-slice T2*-weighted echo planar imaging (EPI: TE/TR = 30/2000 ms, FA = 70°, 32 oblique-axial slices acquired interleaved ascending, with FOV = 200 × 200 mm, 64 × 64 matrix, 4.0 mm slice thickness with 0.5 mm gap, 3.125 × 3.125 mm in-plane resolution, BW=2298 Hz/px), producing a time-series of 195 images at each slice location. During acquisition, athletes were instructed to lie still with their eyes closed and to not focus on anything in particular. Processing and analysis were performed using the Analysis of Functional Neuroimages (AFNI) package (afni.nimh.nih.gov) and customized algorithms developed in the laboratory. After discarding the first 4 volumes to allow scans to reach equilibrium, this included rigid-body motion correction (AFNI 3dvolreg), removal of outlier scan volumes using the SPIKECOR algorithm (nitrc.org/projects/spikecor), slice-timing correction (AFNI 3dTshift), spatial smoothing with a 6 mm Full Width at Half Maximum (FWHM) isotropic 3D Gaussian kernel (AFNI 3dmerge) and regression of motion parameters and linear-quadratic trends as nuisance covariates. For motion parameter regression, Principal Component Analysis was performed on the six rigid-body movement parameters, and the first two principal components were used as nuisance regressors. To control for physiological noise, the data-driven PHYCAA+ algorithm (nitrc.org/projects/phycaa_plus) was used to spatially down-weight areas with non-neural signal, followed by regression of white matter signal. The white matter regression was performed after spatial normalization and the generation of probabilistic tissue maps (see paragraph below for details), by regressing out the mean time-series computed over all white matter voxels (p > 0.95).To perform group-level connectivity analyses, the fMRI data were co-registered to a common anatomical template using the FMRIB Software Library (FSL) package (https://fsl.fmrib.ox.ac.uk). The FSL flirt algorithm was used to compute the rigid-body transform of the mean fMRI volume for each athlete to their T1-weighted anatomical image, along with the 12-parameter affine transformation of the T1 image for each athlete to the MNI152 template. The transformation matrices were then concatenated and the net transform applied to the fMRI data, which was resampled to 4 × 4 × 4 mm resolution to ensure computational tractability for the univariate analyses. To ensure that only grey matter brain regions were analyzed, voxels were retained that intersected with both the MNI152 brain mask and a grey matter mask. The latter was obtained by using the FSL fast algorithm (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST) to segment subject T1 images into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) maps. They were then transformed into MNI152 template space and resampled to 4 × 4 × 4 mm resolution before averaging across subjects. A mask was chosen to include only regions where p(GM) > p(WM) + p(CSF), before eroding the resulting mask using a disk element of diameter 3 voxels. Remaining voxels that overlapped with ventricles on the MNI152 template image were also removed manually. This procedure resulted in 18,401 voxels in the brain of each athlete that were used in subsequent analyses. […]

Pipeline specifications

Software tools AFNI, FSL
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Diseases Brain Injuries