xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05993351</url>
    </required_header>
    <id_info>
      <org_study_id>Concussion MRI &amp; recovery</org_study_id>
      <nct_id>NCT05993351</nct_id>
    </id_info>
    <brief_title>Objective Concussion Assessment Using MRI and Metabolomics</brief_title>
    <official_title>Correlating Advanced MRI Techniques With Neuropsychological Analysis and Immunosensing Assays for Assessment of Sport-related Mild Traumatic Brain Injuries (mTBI)</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>St. Joseph's Healthcare Hamilton</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>McMaster University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>St. Joseph's Healthcare Hamilton</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Mild traumatic brain injury (mTBI), also referred to as concussions, affect millions of&#xD;
        people around the world and can cause harmful long term effects. Unfortunately, concussions&#xD;
        can be hard to diagnose and many people have lasting post-concussion symptoms such as&#xD;
        headaches, difficulty concentrating, and light sensitivity. Recent studies have shown that&#xD;
        advanced magnetic resonance imaging (MRI) techniques can identify subtle brain changes caused&#xD;
        by a concussion. This study aims to track concussions over time measuring MRI brain scans and&#xD;
        post-concussion symptoms to gain a better understand how the brain is affected in comparison&#xD;
        to symptoms.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Mild traumatic brain injuries (mTBI) are a major health concern due to the risk of short and&#xD;
        long-term complications. In order to understand the effects of an TBI (also referred to as a&#xD;
        concussion), studies have examined the physiological and cognitive impact of concussions on&#xD;
        the brains of youth and collegiate athletes. Based on athletic-exposure (AE), the most&#xD;
        concussions in the National Collegiate Athletics Association (NCAA) occur in Men's wrestling,&#xD;
        Men's and Women's ice hockey, Men's football, and Women's soccer (Zuckerman et al. 2015). It&#xD;
        has been estimated that 300 000 sport-related concussions (SRC) occur annually in the United&#xD;
        States among youth and collegiate athletes (Coronado et al. 2015; Gessel et al. 2007;&#xD;
        Langlois et al. 2006; Thurman et al. 1998). However, a SRC estimate would likely be grossly&#xD;
        underestimated due to underreporting and failure to seek medical treatment (Karlin 2011; Kaut&#xD;
        et al. 2003; Kerr et al. 2016; McCrea et al. 2004; Roozenbeek et al. 2013). In reality, the&#xD;
        number of annual SRC could be as high as 1.6 to 3.8 million occurrences (Langlois et al.&#xD;
        2006).&#xD;
  &#xD;
        Brain injuries are classified as mild, moderate or severe based on patient reported symptoms,&#xD;
        cognitive impairment and structural damage visualized using medical imaging (Bodin et al.&#xD;
        2012; DeCuypere and Kilmo 2012; DeMatteo et al. 2010; Roozenbeek et al. 2013). A major&#xD;
        challenge facing mTBI diagnosis has been standardizing assessment, predicting prognosis, and&#xD;
        clearing people to return to work or sport. In order to more accurately diagnose and treat&#xD;
        patients, healthcare providers require a better understanding of how to brain is affected&#xD;
        acutely, and the timeline for when it returns to a pre-concussion state. Recent technological&#xD;
        innovations show promise to supplement the current behavioural and psychological assessments.&#xD;
        Current concussion and mTBI diagnosis are often based on tests that assess a patient's&#xD;
        sensory feedback, mental cognition, motor control, and post-concussion symptoms (Bodin et al.&#xD;
        2012; DeCuypere and Klimo 2012).&#xD;
  &#xD;
        To supplement symptom tracking, magnetic resonance imaging (MRI) has been shown in research&#xD;
        to be an invaluable concussion tool. The health of brain white matter can be predicted based&#xD;
        on the relativistic shape of the myelin surrounding axons and the diffusivity of water along&#xD;
        the length of the axons by using a MRI technique called diffusion tensor imaging (DTI)(Asken&#xD;
        et al. 2018; Jonkman et al. 2015). In addition, the function of brain grey matter can be&#xD;
        assessed using functional magnetic resonance imaging (fMRI) by measuring the paramagnetic&#xD;
        differences between oxygenated and deoxygenated blood, based on the Blood-Oxygen Level&#xD;
        Dependent (BOLD) signal (Horn et al. 2014; Liu et al. 2018; Ogawa et al. 1990). Activated&#xD;
        brain regions have a greater BOLD signal due to magnetic field inhomogeneities caused by&#xD;
        changes in blood volume, blood flow, and local metabolism (Ogawa et al. 1990). An fMRI can be&#xD;
        used to analyze brain resting state activation patterns, a primary connective system is the&#xD;
        Default Mode Network (DMN)(Mak et al. 2017). The DMN has been shown to have decreased&#xD;
        activity following a mTBI (Bonnelle et al. 2011; Zhou et al. 2012).&#xD;
  &#xD;
        A serious issue surrounding head injuries is the need for a method to diagnose athletes&#xD;
        immediately following the injury. The growing interest in using metabolomics for the&#xD;
        discovery of clinically relevant biomarkers associated with mild traumatic brain injury&#xD;
        (mTBI) could be a solution. However, most studies to date have relied exclusively on blood&#xD;
        specimens and/or targeted metabolite panels involving small cohorts of patients without&#xD;
        adequate replication, and validation of aberrant metabolic changes in circulation to&#xD;
        independent MRI-based brain imaging (Fiandaca et al. 2018; Orešič et al. 2016). We propose to&#xD;
        include an analysis of fasting saliva and urine specimens from mTBI patients for&#xD;
        comprehensive metabolite profiling using high throughput multi-segment injection-capillary&#xD;
        electrophoresis-mass spectrometry technology (DiBattista et al. 2019; Yamamoto et al. 2019),&#xD;
        which allows for rapid non-targeted analysis of polar/hydrophilic metabolites, as well as&#xD;
        non-polar/ionic lipids with stringent quality control (Azab et al. 2019).&#xD;
  &#xD;
        This study aims to track concussion recovery over 6-months using clinical standards of&#xD;
        concussion symptoms and objective MRI and metabolomics metrics. Concussion participants will&#xD;
        complete three study visits: acutely within 2-weeks of a concussion, 3-month follow-up and&#xD;
        6-month follow-up. Participants will be recruited from St. Joseph's Healthcare Hamilton and&#xD;
        local athletic organizations. The study protocol will be identical for all three study&#xD;
        visits. Participants will complete the Post-Concussion Symptom Scale (PCSS) and Depression&#xD;
        Anxiety Stress Scale (DASS-42) to measure the presence and self-reported severity of common&#xD;
        post-concussion symptoms. The MRI data will be used to measure brain function (resting state&#xD;
        fMRI) and microstructural properties (diffusion tenor imaging), while the metabolomics will&#xD;
        measure if metabolites have abnormal presence or concentration post-concussion based on urine&#xD;
        and saliva samples. These quantitative methods will be compared to the subjective concussion&#xD;
        symptom scores to identify if brain and physiological abnormalities persist past symptom&#xD;
        resolution, and if certain brain regions are more frequently affected by concussion. It is&#xD;
        hypothesized that across all three time points that brain function will have decreased BOLD&#xD;
        signal fractal complexity and network connectivity (representative of concussion-related&#xD;
        injuries), and white matter damage will be present based on the primary DTI metric of&#xD;
        fractional anisotropy. It is also hypothesized that post-concussion symptoms will be&#xD;
        self-reported as resolved or almost resolved by the 3-month follow-up study visit.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 11, 2023</start_date>
    <completion_date type="Anticipated">December 15, 2025</completion_date>
    <primary_completion_date type="Anticipated">December 15, 2025</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Functional brain characteristics</measure>
      <time_frame>45-minute MRI sessions acutely, 3-months and 6-months post-concussion</time_frame>
      <description>Brain function will be examined based on the network connectivity and fractal complexity of the rsfMRI BOLD signal. This will be measured across known brain networks (e.g., Default Mode Network) and specific regions-of-interest (e.g., insula).</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Microstructural brain characteristics</measure>
      <time_frame>45-minute MRI sessions acutely, 3-months and 6-months post-concussion</time_frame>
      <description>Microstructural brain changes will be examined based on diffusion tensor imaging (DTI) metrics of fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. This will be measured within specific regions-of-interest (e.g., insula).</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Post-concussion symptoms</measure>
      <time_frame>Acutely, 3-months and 6-months post-concussion</time_frame>
      <description>Tracking of self-reported post-concussion symptoms using PCSS and DASS-42 questionnaires</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Metabolomics</measure>
      <time_frame>Acutely, 3-months and 6-months post-concussion</time_frame>
      <description>High throughput multi-segment injection-capillary electrophoresis-mass spectrometry technology will be used to analyze the urine and saliva samples to identify if any polar or non-polar metabolites are abnormal post-concussion, and that may indicate physiological markers for future assessment and treatment options.</description>
    </secondary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">100</enrollment>
    <condition>Concussion, Brain</condition>
    <arm_group>
      <arm_group_label>Concussion</arm_group_label>
      <description>Individuals who have recently sustained a concussion within the past 2-weeks.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Healthy Control</arm_group_label>
      <description>Individuals who have limited to no concussion history, and have not recently sustained a concussion.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Magnetic resonance imaging (MRI)</intervention_name>
      <description>All participants will have 3 MRI scanning sessions to track brain health over time following the same protocol each time. The MRI sessions will occur acutely (&lt;2 weeks post-concussion), 3-months and 6-months post-concussion. A series of MRI scans will be acquired including T1, T2, T2-FLAIR, SWI, ASL, rsfMRI, and DTI scans to characterize structural, microstructural, functional and tissue perfusion changes within the brain over time.</description>
      <arm_group_label>Concussion</arm_group_label>
      <arm_group_label>Healthy Control</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Urine and saliva samples</intervention_name>
      <description>At each study visit all participants will be asked to provide small urine and saliva samples for a metabolomic analysis using a high throughput multi-segment injection-capillary electrophoresis-mass spectrometry technology. This will allow for rapid non-targeted analysis of polar/hydrophilic metabolites, as well as non-polar/ionic lipids with stringent quality control.</description>
      <arm_group_label>Concussion</arm_group_label>
      <arm_group_label>Healthy Control</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Questionnaires</intervention_name>
      <description>The Post-Concussion Symptom Scale (PCSS) and the Depression Anxiety Stress Scale (DASS-42) will be used to assess the self-reported presence and severity of known concussion-related symptoms.</description>
      <arm_group_label>Concussion</arm_group_label>
      <arm_group_label>Healthy Control</arm_group_label>
    </intervention>
    <biospec_retention>Samples Without DNA</biospec_retention>
    <biospec_descr>
      <textblock>
        Urine and saliva samples will be collected during each study visit for all participants to&#xD;
        perform the metabolomic analyses. These samples will be stored in a -80 degree Celcius&#xD;
        freezer until the time of batch analysis, for which the samples will be consumed. Any&#xD;
        remaining biospecimens not consumed in the analyses will be disposed of appropraitely.&#xD;
      </textblock>
    </biospec_descr>
    <eligibility>
      <study_pop>
        <textblock>
          Participants for this prospective observational cohort will include 50 individuals who have&#xD;
          recently sustained a concussion within the last 2 weeks. Participants will primarily be&#xD;
          recruited based on their involvement in collision or contact sport, causing sport-related&#xD;
          concussions. Fifty healthy control participants will be recruited with the intention to&#xD;
          have age, sex and athletic status matching to the concussion participants.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Aged between 9-50&#xD;
  &#xD;
            -  Recently sustained a concussion (within the last 2 weeks)&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Aged 8 and younger or 51 and older&#xD;
  &#xD;
            -  Unable to provide consent (e.g., poor English language skills, etc.)&#xD;
  &#xD;
            -  History of liver or kidney disease&#xD;
  &#xD;
            -  MRI contraindications:&#xD;
  &#xD;
                 -  Pacemaker&#xD;
  &#xD;
                 -  Stent&#xD;
  &#xD;
                 -  Joint prothesis&#xD;
  &#xD;
                 -  Implanted devices&#xD;
  &#xD;
                 -  Claustrophobia&#xD;
  &#xD;
                 -  Pregnant&#xD;
  &#xD;
                 -  Permanent piercings&#xD;
  &#xD;
            -  Chronic/abusive use of alcohol and/or illicit drugs&#xD;
  &#xD;
            -  Previous stroke or moderate/severe traumatic brain injury, subarachnoid hemorrhage, or&#xD;
               intracranial hemorrhage&#xD;
  &#xD;
            -  Healthy control participants must not have a concussion history or recently sustained&#xD;
               a concussion&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>9 Years</minimum_age>
      <maximum_age>50 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Michael D Noseworthy, PhD, PEng</last_name>
      <role>Principal Investigator</role>
      <affiliation>McMaster University</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Dinesh Kumbhare, MD, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>University Health Network, Toronto</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Michael D Noseworthy, PhD, PEng</last_name>
      <phone>905.525.9140</phone>
      <phone_ext>23727</phone_ext>
      <email>nosewor@mcmaster.ca</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Ethan Danielli, PhD</last_name>
      <email>ethan.danielli@uhn.ca</email>
    </overall_contact_backup>
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    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 12, 2023</study_first_submitted>
    <study_first_submitted_qc>August 12, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 15, 2023</study_first_posted>
    <last_update_submitted>August 12, 2023</last_update_submitted>
    <last_update_submitted_qc>August 12, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>St. Joseph's Healthcare Hamilton</investigator_affiliation>
      <investigator_full_name>Michael Noseworthy PhD</investigator_full_name>
      <investigator_title>Director of MRI Research, Imaging Research Centre</investigator_title>
    </responsible_party>
    <keyword>functional magnetic resonance imaging</keyword>
    <keyword>diffusion tensor imaging</keyword>
    <keyword>symptom tracking</keyword>
    <keyword>sport-related concussions</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Brain Concussion</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
      <ipd_description>Study data may be shared upon request to the research team.</ipd_description>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05993351
  org study id: Concussion MRI & recovery
  nct id: NCT05993351
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  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.
  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.
  observational model: Cohort
  time perspective: Prospective
  measure: Functional brain characteristics
  time frame: 45-minute MRI sessions acutely, 3-months and 6-months post-concussion
  description: Brain function will be examined based on the network connectivity and fractal complexity of the rsfMRI BOLD signal. This will be measured across known brain networks (e.g., Default Mode Network) and specific regions-of-interest (e.g., insula).
  measure: Microstructural brain characteristics
  time frame: 45-minute MRI sessions acutely, 3-months and 6-months post-concussion
  description: Microstructural brain changes will be examined based on diffusion tensor imaging (DTI) metrics of fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. This will be measured within specific regions-of-interest (e.g., insula).
  measure: Post-concussion symptoms
  time frame: Acutely, 3-months and 6-months post-concussion
  description: Tracking of self-reported post-concussion symptoms using PCSS and DASS-42 questionnaires
  measure: Metabolomics
  time frame: Acutely, 3-months and 6-months post-concussion
  description: High throughput multi-segment injection-capillary electrophoresis-mass spectrometry technology will be used to analyze the urine and saliva samples to identify if any polar or non-polar metabolites are abnormal post-concussion, and that may indicate physiological markers for future assessment and treatment options.
  arm group label: Concussion
  description: Individuals who have recently sustained a concussion within the past 2-weeks.
  arm group label: Healthy Control
  description: Individuals who have limited to no concussion history, and have not recently sustained a concussion.
  intervention type: Diagnostic Test
  intervention name: Magnetic resonance imaging (MRI)
  description: All participants will have 3 MRI scanning sessions to track brain health over time following the same protocol each time. The MRI sessions will occur acutely (<2 weeks post-concussion), 3-months and 6-months post-concussion. A series of MRI scans will be acquired including T1, T2, T2-FLAIR, SWI, ASL, rsfMRI, and DTI scans to characterize structural, microstructural, functional and tissue perfusion changes within the brain over time.
  arm group label: Concussion
  arm group label: Healthy Control
  intervention type: Diagnostic Test
  intervention name: Urine and saliva samples
  description: At each study visit all participants will be asked to provide small urine and saliva samples for a metabolomic analysis using a high throughput multi-segment injection-capillary electrophoresis-mass spectrometry technology. This will allow for rapid non-targeted analysis of polar/hydrophilic metabolites, as well as non-polar/ionic lipids with stringent quality control.
  arm group label: Concussion
  arm group label: Healthy Control
  intervention type: Behavioral
  intervention name: Questionnaires
  description: The Post-Concussion Symptom Scale (PCSS) and the Depression Anxiety Stress Scale (DASS-42) will be used to assess the self-reported presence and severity of known concussion-related symptoms.
  arm group label: Concussion
  arm group label: Healthy Control
  Urine and saliva samples will be collected during each study visit for all participants to
        perform the metabolomic analyses. These samples will be stored in a -80 degree Celcius
        freezer until the time of batch analysis, for which the samples will be consumed. Any
        remaining biospecimens not consumed in the analyses will be disposed of appropraitely.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 9 Years
  maximum age: 50 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: Michael D Noseworthy, PhD, PEng
  role: Principal Investigator
  affiliation: McMaster University
  last name: Dinesh Kumbhare, MD, PhD
  role: Principal Investigator
  affiliation: University Health Network, Toronto
  last name: Michael D Noseworthy, PhD, PEng
  phone: 905.525.9140
  phone ext: 23727
  email: nosewor@mcmaster.ca
  last name: Ethan Danielli, PhD
  email: ethan.danielli@uhn.ca
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  PMID: 28337734
  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.
  PMID: 30570251
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  PMID: 26330572
  responsible party type: Principal Investigator
  investigator affiliation: St. Joseph's Healthcare Hamilton
  investigator full name: Michael Noseworthy PhD
  investigator title: Director of MRI Research, Imaging Research Centre
  mesh term: Brain Concussion
  sharing ipd: Undecided
  ipd description: Study data may be shared upon request to the research team.