Study Design and Procedure
Data were drawn from two pediatric neuroimaging studies conducted on the same MRI scanner at the Alberta Children’s Hospital in Calgary, AB between September 2016 and July 2019. Both studies were conducted with the approval of the conjoint health research ethics board at the University of Calgary; all participants provided informed assent when appropriate and parents or guardians provided written informed consent. The protocols for both studies are published elsewhere (Geeraert, Lebel, and Lebel 2019; Yeates, Beauchamp, Craig, Doan, Zemek, B. H. Bjornson, et al. 2017). In the interest of space, only the relevant methodology for the present study is described in detail below.
Children with mTBI or OI between the ages of 8.00-16.99 years were recruited and assessed as part of the Advancing Concussion Assessment in Pediatrics (A-CAP) study, a large multi-site study of pediatric mTBI that included a post-acute assessment with longitudinal follow-up (Yeates, Beauchamp, Craig, Doan, Zemek, B. H. Bjornson, et al. 2017). For both groups, acute injury signs and symptoms were assessed within 48 hours post-injury at the time of enrollment in the emergency department (ED) at Alberta Children’s Hospital, where parents also completed demographic questionnaires. Injured children returned for a post-acute assessment that included a 3T MRI scan and was targeted for 10 days post-injury.
The TD comparison group was comprised of healthy children who were recruited from the community as part of a study of typical brain development in childhood and adolescence (Geeraert et al. 2019).
A total of 226 children (mTBI/OI = 150/76) were recruited as part of the larger A-CAP study. Of those enrolled, 191 (mTBI/OI = 130/61) children returned for the post-acute assessment. Children who returned for the post-acute assessment did not significantly differ from those who did not (mTBI/OI = 20/15) in terms of sex, race, or age at time of injury, all p 0.483. At the post-acute assessment, 147 children completed an MRI (mTBI/OI = 98/49). One child with MRI data withdrew from the parent study and was not included in this study. The children who returned for the post-acute assessment and completed the post-acute MRI did not significantly differ from participants who returned but did not complete an MRI (mTBI/OI = 32/12) in terms of premorbid or post-acute somatic or cognitive symptoms, race, sex, or age at time of injury, all p 0.289. Orthodontia and scheduling difficulties were the most common reasons that MRI was not completed.
Mild TBI group. Children in the mTBI group (n = 98) sustained a blunt head trauma resulting in at least one of the following three criteria, consistent with the World Health Organization (WHO) definition of mTBI: an observed loss of consciousness (LOC), a Glasgow Coma Scale (GCS) score of 13 or 14, or at least one acute sign or symptom of concussion as noted by ED medical personnel on a standard case report form, such as post-traumatic amnesia (PTA), focal neurological deficits, vomiting, headache, dizziness, or other mental status changes (Carroll et al. 2004; Cassidy et al. 2004; Teasdale and Jennett 1974). Children were excluded if they demonstrated delayed neurological deterioration (i.e., GCS < 13), required neurosurgical intervention, or had LOC > 30 min or PTA > 24 hr.
Mild OI group. Children with OI (n = 49) were included if they sustained an upper or lower extremity fracture, sprain, or strain due to blunt force/physical trauma, associated with an Abbreviated Injury Scale (AIS) score 4 (Committee on Injury Scaling 1998). Children were excluded from the OI group if they had head trauma or symptoms of concussion, or any injury requiring surgical intervention or procedural sedation.
Exclusion criteria for both injury groups were any other severe injury that resulted in an AIS score > 4; prior concussion within the past 3 months; hypoxia, hypotension, or shock during or following the injury; history of previous TBI requiring hospitalization; premorbid neurological disorder or intellectual disability; injury resulting from non-accidental trauma; history of severe psychiatric disorder requiring hospitalization; or any MRI contraindications. Additional inclusion/exclusion criteria are described in the published study protocol (Yeates, Beauchamp, Craig, Doan, Zemek, B. H. Bjornson, et al. 2017).
TD group. Children in the TD group (n = 41) were recruited from the Calgary community. All had uncomplicated birth histories and were born between 37-42 weeks gestational age. Participants were excluded if they had a history of neurodevelopmental or intellectual disability, neurological or psychiatric disorder, or MRI contraindication (Geeraert et al. 2018, 2019). Of an initial 53 enrolled participants in the parent TD study, 12 were excluded from this study for having an age outside of the A-CAP study age range (i.e., age outside of 8.00-16.99 years).
Magnetic Resonance Imaging
All participants completed 3T MRI without sedation on the same General Electric MR750w 3T scanner with a 32-channel head coil (GE, Milwaukee, WI). Children in the mTBI and OI groups completed MRI 2-23 days post-injury (M = 8.93, Mdn = 8.62, SD = 3.51), with most scans (75%) completed 6-10 days post-injury. Time between injury and the post-acute MRI scan did not differ between the mTBI and OI groups (see Table 1).
Image acquisition. T1-weighted MRI data were acquired using a fast spoiled gradient echo brain volume (FSPGR BRAVO) in the axial plane with flip angle = 10, inversion time (TI)/repetition time (TR)/echo time (TE) = 600/8.25/3.2 ms, resolution = 0.8 mm, field of view (FOV) = 512 cm, contiguous slices = 226, and scan duration = 5:28 min. Diffusion-weighted images were acquired using a spin echo EPI sequence with 5 b = 0 s/mm2 volumes and 30 gradient directions at b = 900 s/mm2 , TR/TE = 12000/88-98 ms, resolution = 2.2 mm, FOV = 256 cm, contiguous slices = 57, and scan duration = 7:12 min.
Image processing. T1- and diffusion-weighted DICOM data were converted into NIfTI format using the dcm2niix tool in MRIcron (publicly available software; https://github.com/rordenlab/dcm2niix), and the bval and bvec files were automatically created from the raw diffusion-weighted DICOM headers. During conversion to NIfTI format, T1-weighted images were automatically reoriented to canonical space. Subsequent processing procedures were completed on a remote Linux computing cluster at the University of Calgary in AB, Canada. Cortical reconstruction of the T1-weighted image was performed for all subjects using FreeSurfer v6.0.0 (Fischl 2012).
Initial quality review. Initial visual review of both image types was conducted to identify and exclude any data with incidental findings (n = 2), scanner artifact such as aliasing or warping (T1/diffusion = 1/3), data collected without the default scan parameters (T1/diffusion = 1/10), and any incomplete or partially acquired images (T1/diffusion = 1/3). Therefore, a total of 183 (mTBI/OI/TD = 97/45/41) T1-weighted and 170 (mTBI/OI/TD = 89/42/39) diffusion-weighted datasets were included in subsequent analyses after the initial quality review.
Qualitative Ratings of Motion Artifacts. After the initial review, all remaining datasets were manually inspected for motion in accordance with published protocols (Reuter et al. 2015; Roalf et al. 2016; Rosen et al. 2018). T1-weighted images were visually inspected by at least two trained analysts and rated using a 0-2 ordinal scale, with “0” assigned to images with gross artifacts that were considered unusable, “1” assigned to images with minor artifacts that were judged acceptable for analysis, and “2” assigned to images that were free from visible artifact and were considered to be of excellent quality. For diffusion-weighted images, each volume (gradient direction) was visually inspected for motion to determine the volumes to exclude. The diffusion-weighted images were then classified on a 0-2 rating scale based on total number of volumes with identified motion artifacts; scans that had >7 (i.e., > 25%) volumes with visible motion-related artifacts were rated unusable (rating of 0), scans that contained at 1-7 volumes with motion artifacts were rated acceptable (rating of 1), and scans with no volumes with motion artifacts were considered excellent quality (rating of 2). Exemplars for each MRI sequence are shown in Figure 1.
Quantitative Ratings of Motion Artifacts. The Euler number, which is automatically produced by the FreeSurfer processing pipeline, was used as a quantitative estimate of motion artifacts during T1-weighted MRI data acquisition. The Euler number captures the topological complexity of the reconstructed cortical surface by calculating the sum of the vertices and faces subtracted by the number of faces (Dale, Fischl, and Sereno 1999). This measure robustly correlated with manual ratings of motion artifacts in anatomical MRI data drawn from a large developmental neuroimaging study (Rosen et al. 2018). Euler numbers are negative, with higher values (i.e., closer to 0) indicating less motion.
The eddy tool from the FSL v6.0.1 Diffusion Toolbox (FDT diffusion) v5.0 was used to derive quantitative measures of motion artifacts during diffusion-weighted MRI data acquisition (Andersson and Sotiropoulos 2016). The output.eddy_movement_rms parameter was used as a measure of total movement. This parameter estimates motion between volumes, calculated as the restricted root mean square (RMS) displacement of each voxel compared to the previous volume across the total number of voxels within the brain; it attempts to isolate RMS displacement caused by in-scanner motion from eddy current-related artifacts.
Diffusion Tensor Imaging
To determine whether the removal of diffusion-weighted volumes (gradients) with severe motion artifacts influenced DTI metrics, mean FA and MD values were estimated for the participants who had 1 bad volume on diffusion-weighted imaging (n = 68) in two ways: (i) from the uncorrected data (i.e., using all 35 volumes, including those identified as having gross motion artifacts) and (ii) from the corrected data (i.e., 35 volumes, excluding those identified as having gross motion artifacts).
The diffusion-weighted images were eddy current and motion corrected, skull-stripped, and tensor fitted using ExploreDTI v4.8.6 running on MATLAB v8.6.0 R2018a (MathWorks Inc., Natick, MA). T1-weighted images were brain-extracted using the Advanced Normalization Tools version 22.214.171.124.dev13-ga16cc (compiled January 18, 2019) volume-based cortical thickness estimation pipeline (antsCorticalThickness.sh) with the OASIS pediatric template from the MICCAI 2012 Multi Atlas Challenge (Tustison et al. 2014), and used for anatomical reference during skull-stripping. White matter pathways were derived for the genu, body, and splenium of the corpus callosum and for the left and right corticospinal tract and cingulum bundle using a semi-automated tractography approach. These tracts were chosen given that they run inferior-superior, left-right, and anterior-posterior. Average FA and MD were extracted within ExploreDTI for each tract for each participant.
Demographic data were analyzed using analysis of variance (ANOVA) for continuous variables and chi-square techniques for categorical variables. Multiple multivariable logistic and linear regressions were used to investigate the relations of group (mTBI, OI, TD), age, sex, and their interactions to qualitative and quantitative estimates of motion. Non-significant (p > .05) interactions were removed from final models. Area under the curve (AUC) analyses were used to evaluate the concordance among qualitative and quantitative ratings. Linear mixed effects modeling and Pearson correlation were used to examine the effect of volume (gradient) removal on FA and MD values of selected white matter tracts, with motion correction status (uncorrected, corrected), hemisphere (or region for corpus callosum subregions), group, and their interaction included as fixed effects and participant as a random effect, with covariates sex and age.
A post-hoc power analysis, conducted using G*Power v3.1 (Faul et al. 2009), indicated that the current sample size (N = 188) was sufficiently powered (1-b = .95) to detect small effects (partial R2 = .10) with 4 predictors at a critical F-value = 3.05 and = .05.