Clinical trial NCT05993351: Objective Concussion Assessment Using MRI and Metabolomics
Official title: Correlating Advanced MRI Techniques With Neuropsychological Analysis and Immunosensing Assays for Assessment of Sport-related Mild Traumatic Brain Injuries (mTBI)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Concussion, Brain
Keywords: functional magnetic resonance imaging
diffusion tensor imaging
symptom tracking
sport-related concussions
Lead sponsor: St. Joseph's Healthcare Hamilton
Sponsor class: OTHER
Collaborators: name: McMaster University
First posted: 2023-08-15
Enrollment: 100
Sex: ALL
Minimum age (years): 9
Maximum age (years): 50
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants for this prospective observational cohort will include 50 individuals who have recently sustained a concussion within the last 2 weeks. Participants will primarily be recruited based on their involvement in collision or contact sport, causing sport-related concussions. Fifty healthy control participants will be recruited with the intention to have age, sex and athletic status matching to the concussion participants.

Brief summary: Mild traumatic brain injury (mTBI), also referred to as concussions, affect millions of people around the world and can cause harmful long term effects. Unfortunately, concussions can be hard to diagnose and many people have lasting post-concussion symptoms such as headaches, difficulty concentrating, and light sensitivity. Recent studies have shown that advanced magnetic resonance imaging (MRI) techniques can identify subtle brain changes caused by a concussion. This study aims to track concussions over time measuring MRI brain scans and post-concussion symptoms to gain a better understand how the brain is affected in comparison to symptoms.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Aged between 9-50
* Recently sustained a concussion (within the last 2 weeks)

Exclusion Criteria:

* Aged 8 and younger or 51 and older
* Unable to provide consent (e.g., poor English language skills, etc.)
* History of liver or kidney disease
* MRI contraindications:

  * Pacemaker
  * Stent
  * Joint prothesis
  * Implanted devices
  * Claustrophobia
  * Pregnant
  * Permanent piercings
* Chronic/abusive use of alcohol and/or illicit drugs
* Previous stroke or moderate/severe traumatic brain injury, subarachnoid hemorrhage, or intracranial hemorrhage
* Healthy control participants must not have a concussion history or recently sustained a concussion

Primary outcomes:
measure: Functional brain characteristics
timeFrame: 45-minute MRI sessions acutely, 3-months and 6-months post-concussion
measure: Microstructural brain characteristics
timeFrame: 45-minute MRI sessions acutely, 3-months and 6-months post-concussion
measure: Post-concussion symptoms
timeFrame: Acutely, 3-months and 6-months post-concussion
Secondary outcomes:
measure: Metabolomics
timeFrame: Acutely, 3-months and 6-months post-concussion
Other outcomes:
Locations:
city: Hamilton
country: Canada
facility: Imaging Research Center at St. Joseph's Healthcare Hamilton
state: Ontario
Study officials:
affiliation: McMaster University
name: Michael D Noseworthy, PhD, PEng
role: PRINCIPAL_INVESTIGATOR
affiliation: University Health Network, Toronto
name: Dinesh A Kumbhare, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Asken BM, DeKosky ST, Clugston JR, Jaffee MS, Bauer RM. Diffusion tensor imaging (DTI) findings in adult civilian, military, and sport-related mild traumatic brain injury (mTBI): a systematic critical review. Brain Imaging Behav. 2018 Apr;12(2):585-612. doi: 10.1007/s11682-017-9708-9.
citation: Azab S, Ly R, Britz-McKibbin P. Robust Method for High-Throughput Screening of Fatty Acids by Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry with Stringent Quality Control. Anal Chem. 2019 Feb 5;91(3):2329-2336. doi: 10.1021/acs.analchem.8b05054. Epub 2019 Jan 7.
citation: Bodin, D., Yeates, K. O., & Klamar, K. (2012). Definition and Classification of Concussion. In J. N. Apps & K. D. Walter (Eds.), Pediatric and Adolescent Concussion (pp. 9-19). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-89545-1_2
citation: Bonnelle V, Leech R, Kinnunen KM, Ham TE, Beckmann CF, De Boissezon X, Greenwood RJ, Sharp DJ. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J Neurosci. 2011 Sep 21;31(38):13442-51. doi: 10.1523/JNEUROSCI.1163-11.2011.
citation: Coronado VG, Haileyesus T, Cheng TA, Bell JM, Haarbauer-Krupa J, Lionbarger MR, Flores-Herrera J, McGuire LC, Gilchrist J. Trends in Sports- and Recreation-Related Traumatic Brain Injuries Treated in US Emergency Departments: The National Electronic Injury Surveillance System-All Injury Program (NEISS-AIP) 2001-2012. J Head Trauma Rehabil. 2015 May-Jun;30(3):185-97. doi: 10.1097/HTR.0000000000000156.
citation: Decuypere M, Klimo P Jr. Spectrum of traumatic brain injury from mild to severe. Surg Clin North Am. 2012 Aug;92(4):939-57, ix. doi: 10.1016/j.suc.2012.04.005. Epub 2012 Jun 5.
citation: Dematteo CA, Hanna SE, Mahoney WJ, Hollenberg RD, Scott LA, Law MC, Newman A, Lin CY, Xu L. "My child doesn't have a brain injury, he only has a concussion". Pediatrics. 2010 Feb;125(2):327-34. doi: 10.1542/peds.2008-2720. Epub 2010 Jan 18.
citation: DiBattista A, McIntosh N, Lamoureux M, Al-Dirbashi OY, Chakraborty P, Britz-McKibbin P. Metabolic Signatures of Cystic Fibrosis Identified in Dried Blood Spots For Newborn Screening Without Carrier Identification. J Proteome Res. 2019 Mar 1;18(3):841-854. doi: 10.1021/acs.jproteome.8b00351. Epub 2019 Jan 7.
citation: Fiandaca MS, Mapstone M, Mahmoodi A, Gross T, Macciardi F, Cheema AK, Merchant-Borna K, Bazarian J, Federoff HJ. Plasma metabolomic biomarkers accurately classify acute mild traumatic brain injury from controls. PLoS One. 2018 Apr 20;13(4):e0195318. doi: 10.1371/journal.pone.0195318. eCollection 2018.
citation: Gessel LM, Fields SK, Collins CL, Dick RW, Comstock RD. Concussions among United States high school and collegiate athletes. J Athl Train. 2007 Oct-Dec;42(4):495-503.
citation: Horn A, Ostwald D, Reisert M, Blankenburg F. The structural-functional connectome and the default mode network of the human brain. Neuroimage. 2014 Nov 15;102 Pt 1:142-51. doi: 10.1016/j.neuroimage.2013.09.069. Epub 2013 Oct 4.
citation: Jonkman LE, Rosenthal DM, Sormani MP, Miles L, Herbert J, Grossman RI, Inglese M. Gray Matter Correlates of Cognitive Performance Differ between Relapsing-Remitting and Primary-Progressive Multiple Sclerosis. PLoS One. 2015 Oct 20;10(10):e0129380. doi: 10.1371/journal.pone.0129380. eCollection 2015.
citation: Karlin AM. Concussion in the pediatric and adolescent population: "different population, different concerns". PM R. 2011 Oct;3(10 Suppl 2):S369-79. doi: 10.1016/j.pmrj.2011.07.015.
citation: Kaut KP, DePompei R, Kerr J, Congeni J. Reports of head injury and symptom knowledge among college athletes: implications for assessment and educational intervention. Clin J Sport Med. 2003 Jul;13(4):213-21. doi: 10.1097/00042752-200307000-00004.
citation: Kerr ZY, Register-Mihalik JK, Kroshus E, Baugh CM, Marshall SW. Motivations Associated With Nondisclosure of Self-Reported Concussions in Former Collegiate Athletes. Am J Sports Med. 2016 Jan;44(1):220-5. doi: 10.1177/0363546515612082. Epub 2015 Nov 18.
citation: Langlois JA, Rutland-Brown W, Wald MM. The epidemiology and impact of traumatic brain injury: a brief overview. J Head Trauma Rehabil. 2006 Sep-Oct;21(5):375-8. doi: 10.1097/00001199-200609000-00001.
citation: Liu X, Zhang N, Chang C, Duyn JH. Co-activation patterns in resting-state fMRI signals. Neuroimage. 2018 Oct 15;180(Pt B):485-494. doi: 10.1016/j.neuroimage.2018.01.041. Epub 2018 Feb 21.
citation: Mak LE, Minuzzi L, MacQueen G, Hall G, Kennedy SH, Milev R. The Default Mode Network in Healthy Individuals: A Systematic Review and Meta-Analysis. Brain Connect. 2017 Feb;7(1):25-33. doi: 10.1089/brain.2016.0438. Epub 2017 Jan 9.
citation: McCrea M, Prichep L, Powell MR, Chabot R, Barr WB. Acute effects and recovery after sport-related concussion: a neurocognitive and quantitative brain electrical activity study. J Head Trauma Rehabil. 2010 Jul-Aug;25(4):283-92. doi: 10.1097/HTR.0b013e3181e67923.
citation: Ogawa S, Lee TM, Nayak AS, Glynn P. Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med. 1990 Apr;14(1):68-78. doi: 10.1002/mrm.1910140108.
citation: Oresic M, Posti JP, Kamstrup-Nielsen MH, Takala RSK, Lingsma HF, Mattila I, Jantti S, Katila AJ, Carpenter KLH, Ala-Seppala H, Kyllonen A, Maanpaa HR, Tallus J, Coles JP, Heino I, Frantzen J, Hutchinson PJ, Menon DK, Tenovuo O, Hyotylainen T. Human Serum Metabolites Associate With Severity and Patient Outcomes in Traumatic Brain Injury. EBioMedicine. 2016 Oct;12:118-126. doi: 10.1016/j.ebiom.2016.07.015. Epub 2016 Jul 15.
citation: Roozenbeek B, Maas AI, Menon DK. Changing patterns in the epidemiology of traumatic brain injury. Nat Rev Neurol. 2013 Apr;9(4):231-6. doi: 10.1038/nrneurol.2013.22. Epub 2013 Feb 26.
citation: Thurman DJ, Branche CM, Sniezek JE. The epidemiology of sports-related traumatic brain injuries in the United States: recent developments. J Head Trauma Rehabil. 1998 Apr;13(2):1-8. doi: 10.1097/00001199-199804000-00003.
citation: Yamamoto M, Pinto-Sanchez MI, Bercik P, Britz-McKibbin P. Metabolomics reveals elevated urinary excretion of collagen degradation and epithelial cell turnover products in irritable bowel syndrome patients. Metabolomics. 2019 May 20;15(6):82. doi: 10.1007/s11306-019-1543-0.
citation: Zhou Y, Milham MP, Lui YW, Miles L, Reaume J, Sodickson DK, Grossman RI, Ge Y. Default-mode network disruption in mild traumatic brain injury. Radiology. 2012 Dec;265(3):882-92. doi: 10.1148/radiol.12120748.
citation: Zuckerman SL, Kerr ZY, Yengo-Kahn A, Wasserman E, Covassin T, Solomon GS. Epidemiology of Sports-Related Concussion in NCAA Athletes From 2009-2010 to 2013-2014: Incidence, Recurrence, and Mechanisms. Am J Sports Med. 2015 Nov;43(11):2654-62. doi: 10.1177/0363546515599634. Epub 2015 Sep 1.