Mild traumatic brain injuries (mTBI) are a major health concern due to the risk of short
and long-term complications. In order to understand the effects of an TBI (also referred
to as a concussion), studies have examined the physiological and cognitive impact of
concussions on the brains of youth and collegiate athletes. Based on athletic-exposure
(AE), the most concussions in the National Collegiate Athletics Association (NCAA) occur
in Men's wrestling, Men's and Women's ice hockey, Men's football, and Women's soccer
(Zuckerman et al. 2015). It has been estimated that 300 000 sport-related concussions
(SRC) occur annually in the United States among youth and collegiate athletes (Coronado
et al. 2015; Gessel et al. 2007; Langlois et al. 2006; Thurman et al. 1998). However, a
SRC estimate would likely be grossly underestimated due to underreporting and failure to
seek medical treatment (Karlin 2011; Kaut et al. 2003; Kerr et al. 2016; McCrea et al.
2004; Roozenbeek et al. 2013). In reality, the number of annual SRC could be as high as
1.6 to 3.8 million occurrences (Langlois et al. 2006).
Brain injuries are classified as mild, moderate or severe based on patient reported
symptoms, cognitive impairment and structural damage visualized using medical imaging
(Bodin et al. 2012; DeCuypere and Kilmo 2012; DeMatteo et al. 2010; Roozenbeek et al.
2013). A major challenge facing mTBI diagnosis has been standardizing assessment,
predicting prognosis, and clearing people to return to work or sport. In order to more
accurately diagnose and treat patients, healthcare providers require a better
understanding of how to brain is affected acutely, and the timeline for when it returns
to a pre-concussion state. Recent technological innovations show promise to supplement
the current behavioural and psychological assessments. Current concussion and mTBI
diagnosis are often based on tests that assess a patient's sensory feedback, mental
cognition, motor control, and post-concussion symptoms (Bodin et al. 2012; DeCuypere and
Klimo 2012).
To supplement symptom tracking, magnetic resonance imaging (MRI) has been shown in
research to be an invaluable concussion tool. The health of brain white matter can be
predicted based on the relativistic shape of the myelin surrounding axons and the
diffusivity of water along the length of the axons by using a MRI technique called
diffusion tensor imaging (DTI)(Asken et al. 2018; Jonkman et al. 2015). In addition, the
function of brain grey matter can be assessed using functional magnetic resonance imaging
(fMRI) by measuring the paramagnetic differences between oxygenated and deoxygenated
blood, based on the Blood-Oxygen Level Dependent (BOLD) signal (Horn et al. 2014; Liu et
al. 2018; Ogawa et al. 1990). Activated brain regions have a greater BOLD signal due to
magnetic field inhomogeneities caused by changes in blood volume, blood flow, and local
metabolism (Ogawa et al. 1990). An fMRI can be used to analyze brain resting state
activation patterns, a primary connective system is the Default Mode Network (DMN)(Mak et
al. 2017). The DMN has been shown to have decreased activity following a mTBI (Bonnelle
et al. 2011; Zhou et al. 2012).
A serious issue surrounding head injuries is the need for a method to diagnose athletes
immediately following the injury. The growing interest in using metabolomics for the
discovery of clinically relevant biomarkers associated with mild traumatic brain injury
(mTBI) could be a solution. However, most studies to date have relied exclusively on
blood specimens and/or targeted metabolite panels involving small cohorts of patients
without adequate replication, and validation of aberrant metabolic changes in circulation
to independent MRI-based brain imaging (Fiandaca et al. 2018; Orešič et al. 2016). We
propose to include an analysis of fasting saliva and urine specimens from mTBI patients
for comprehensive metabolite profiling using high throughput multi-segment
injection-capillary electrophoresis-mass spectrometry technology (DiBattista et al. 2019;
Yamamoto et al. 2019), which allows for rapid non-targeted analysis of polar/hydrophilic
metabolites, as well as non-polar/ionic lipids with stringent quality control (Azab et
al. 2019).
This study aims to track concussion recovery over 6-months using clinical standards of
concussion symptoms and objective MRI and metabolomics metrics. Concussion participants
will complete three study visits: acutely within 2-weeks of a concussion, 3-month
follow-up and 6-month follow-up. Participants will be recruited from St. Joseph's
Healthcare Hamilton and local athletic organizations. The study protocol will be
identical for all three study visits. Participants will complete the Post-Concussion
Symptom Scale (PCSS) and Depression Anxiety Stress Scale (DASS-42) to measure the
presence and self-reported severity of common post-concussion symptoms. The MRI data will
be used to measure brain function (resting state fMRI) and microstructural properties
(diffusion tenor imaging), while the metabolomics will measure if metabolites have
abnormal presence or concentration post-concussion based on urine and saliva samples.
These quantitative methods will be compared to the subjective concussion symptom scores
to identify if brain and physiological abnormalities persist past symptom resolution, and
if certain brain regions are more frequently affected by concussion. It is hypothesized
that across all three time points that brain function will have decreased BOLD signal
fractal complexity and network connectivity (representative of concussion-related
injuries), and white matter damage will be present based on the primary DTI metric of
fractional anisotropy. It is also hypothesized that post-concussion symptoms will be
self-reported as resolved or almost resolved by the 3-month follow-up study visit.
