Description

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.