Introduction
Traumatic Brain Injury (TBI) is a significant public health and economic issue throughout the entire world. In high-income nations, it is the leading cause of death and disability in children and young adults (Maas, 2008). Over the past several decades, incidence has risen worldwide due to increased private transportation by car and bicycle in lower income nations. In North America and Europe, incidence rates through transportation accident have been reduced through safety precautions but remain high through high levels of youth participation in sports. 1.6 to 3.8 million incidences of sport-related TBI were reported in 2006 (Jean A Langlois, Wesley Rutland-Brown, & Marlena M Wald, 2006). Sports-related injuries are only a fraction of TBI cases in the world annually, and incidence rates have been growing overall. In the United States, 1.3 million visits to the emergency room, 52,000 deaths, and 124,000 disabilities were reported annually between 2002-2007 (Lagbas, Bazargan-Hejazi, Shaheen, Kermah, &; Pan, 2013). In Europe, 235 per 100,000 hospitalizations result from TBI with a mortality rate of 15 per 100,000 (Tagliaferri, Compagnone, Korsic, Servadei, &; Kraus, 2006).
Due to the high incidence of TBI throughout the world, the economic impact can also be severe. In the United States where public health care has come to the forefront of public consciousness, the costs of TBI can have a major impact on public policy. Within the United States, healthcare costs associated with incidence and continued treatment of patients with TBI has been estimated at $60 billion per year (Thurman, 2001). Similar costs can be estimated throughout much of the industrialized world. Such economic costs make diagnosis and protection against TBI much more important, not only for researchers but for politicians and physicians as well.
The economic and physical impacts of TBI are directly related to the severity of the injury. TBI is generally classified with three levels of severity: mild, moderate, and severe. In the past, many differing models have been used to classify the severity of TBI, but recently physicians and first responders have leaned towards utilizing solely the Glasgow Coma Scale (GCS) to classify TBIs. The Glasgow Coma Scale is a scale of 3-15 with three areas being measured: motor response (1-6), verbal response (1-5), and eye opening (1-4). From these scores, a score range of 13-15 is classified as mild TBI, 9-12 is classified as moderate TBI, and 3-8 is classified as severe TBI. Mortality is generally only seen in patients with a GCS score of 8 or below, or only in the severe TBI category (Liebert, 2000).
Traumatic Brain Injuries have significant importance when found or studied in the pediatric populations. TBIs in the developing brain can be particularly dangerous if they interrupt or retard growth processes. Pediatrics may also be at a greater risk of TBI with over 500,000 emergency room visits being made annually for pediatric TBI (aap, 2017). Over $1 billion is spent annually on healthcare for these pediatric TBI patients (Schneier, Shields, Hostetler, Xiang, & Smith, 2006). The outcomes in pediatric TBI has been extensively researched with findings being relatively conclusive and alarming. One such study found that TBI in pediatrics leads to cognitive deficits, behavioral problems, low academic performance, and poorer overall health (Yeates et al., 2001). Another study found that with increasing TBI severity, there were poorer social outcomes in youth. These social outcomes included low social competence, group interaction, and social problems in testing (Yeates et al., 2004). With significant declines in cognition, behavior, and overall health seen in youth TBI, it becomes imperative to understand the underlying physiological and neuroanatomical basis of TBI in order to better understand and treat these patients.
One area of focus in studying TBI has been ventricular size and volume. The ventricular system is a group of four cavities in the cerebrum where CSF is produced and circulated throughout the skull and spine. Ventricular volume is key in measuring overall brain health due to the main reason for its volumetric expansion being the decrease in cerebral gray matter that border the ventricles. Thus, astroturfing research in TBIs looked into ventricular size and volume with varied results. One study found that among patients suffering TBI, a significant increase in lateral ventricle and third ventricle volume was seen that increased over time (Blatter et al., 1997). In opposition to this study, another found that among high school athletes who suffered repeated minor TBI, there was no increase in ventricular volume or size (Terri and Miller, 2017). A third study, more relevant to our study design found a 1.6% increase in lateral ventricle volume following pediatric TBI (Dennis et al., 2016). One possible reason for the variance in these studies could be explained in the severity of TBI patients tested; those studies with more severe TBI patients saw more significant changes in ventricular size and shape.
This study aims to study ventricular size in pediatric patients who have suffered moderate TBI and help determine if severity is linked to ventricular volume and length increases. We hypothesize that small but statistically significant increases in lateral ventricular volume and third ventricle length will be seen then comparing our moderate TBI group to our control group.
Methods
Participants
The participants utilized for this experiment were cases from a multi-site study of social outcomes in children after suffering a traumatic brain injury. These participants were found in conjunction with the Social Outcome of Brain Injury in Kids (SOBIK) group. All subjects were school aged children and were matched to orthopedic injured controls who had also been injured and hospitalized. All members of the control group had never experienced loss of consciousness or had any known head injury, those who had were excluded from this study. All participants from the experimental group suffered from a moderate Traumatic Brain Injury.
Neuroimaging Procedures
A 1.5T GE Signa Excite Scanner with an eight-channel head coil was used to acquire a series of T1-weighted brain images. The parameters of the scans utilized a three-dimensional magnetization-prepared rapid gradient echo pulse sequence with a TR of 8.9 ms and a TW of 3.8 ms. 166 contiguous sagittal slices were obtained at an in-plane resolution of 0.47 x 0.47 mm, a slice thickness of 1.2 mm, an acquisition matrix of 512 x 512 mm, a FOV of 192 mm, and a flip angle of 8o.
All data processing was completed on the Mary Lou Fulton Supercomputer at Brigham Young University. The Fulton Supercomputing Lab maintains 21,552 CPU cores and 972 compute nodes, all running Red Hat Enterprise Linux 6.6. For more information and all technical details of the supercomputing system please refer to:
https://marylou.byu.edu/documentation/resources.
MRI Preprocessing
In order to standardize the images that were obtained, a series of preprocessing steps were necessary. The first step was to convert the images obtained by the MRI scanner in a DICOM format into a NIfTI format that enables editing and normalization through the dcm2nii program (NITRC, Chris Rorden) which was downloaded from the following URL:
https://www.nitrc.org/frs/?group_id=880. The version of dcm2niix utilized in this study was version v1.0.20170821 and the code entered was as follows:
dcm2niix -o $subjDir/t1 -x y -z n -f t1 $subjDir/DICOM
In addition to converting formats, the -x option of the code allowed the program to reorient and crop the images to a more anatomical position. Due to different scanning parameters, this reorientation brings all scans to have the same directions for x, y, and z coordinates and aligns it as if all scans had been taken from a coronal perspective. The x coordinates go from left to right in a negative to positive direction, y becomes positive moving posterior, and z coordinates become more positive moving superior. The cropping is also crucial in order to remove excess space from the field of view. The -z flag is the option to zip or compress the file. The -f flag shows the pathway to the image to be processed.
The next step in image preprocessing involved aligning the scans along the anterior and posterior commissures. Such an alignment is necessary due to the varied positioning of the subjects within the magnetic field of the scanner. The program utilized, acpcdetect, is part of the Automatic Registration Toolbox program (NITRC) and was downloaded from the following URL:
https://www.nitrc.org/frs/?group_id=90. The version of acpcdetect utilized was 2011-04-05. The code utilized was as follows:
art/acpcdetect -M -o $subjDir/t1/acpc.nii -i $subjDir/t1/t1.nii
The -M option commands the program to make the midpoint between AC and PC the center of the output field of view. This alignment in addition to reorientation done in the dcm2niix program allows all subjects to have an identical orientation in the FOV and midline as shown in the figure below.