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/NCT06025201</url>
    </required_header>
    <id_info>
      <org_study_id>71778</org_study_id>
      <secondary_id>1K23AR083171-01</secondary_id>
      <nct_id>NCT06025201</nct_id>
    </id_info>
    <brief_title>Detection of EEG-Based Biomarkers of Chronic Low Back Pain</brief_title>
    <official_title>Characterization of Longitudinal EEG Biomarkers in Chronic Low Back Pain</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Stanford University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)</agency>
        <agency_class>NIH</agency_class>
      </collaborator>
    </sponsors>
    <source>Stanford University</source>
    <oversight_info>
      <has_dmc>Yes</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>
        Chronic low back pain (CLBP) is a pervasive disorder affecting up to one-fifth of adults&#xD;
        globally and is the single greatest cause of disability worldwide. Despite the high&#xD;
        prevalence and detrimental impact of CLBP, its treatments and mechanisms remain largely&#xD;
        unclear. Biomarkers that predict symptom progression in CLBP support precision-based&#xD;
        treatments and ultimately aid in reducing suffering. Longitudinal brain-based resting-state&#xD;
        neuroimaging of patients with CLBP has revealed neural networks that predict pain&#xD;
        chronification and its symptom progression. Although early findings suggest that measurements&#xD;
        of brain networks can lead to the development of prognostic biomarkers, the predictive&#xD;
        ability of these models is strongest for short-term follow-up. Measurements of different&#xD;
        neural systems may provide additional benefits with better predictive power.&#xD;
  &#xD;
        Emotional and cognitive dysfunction is common in CLBP, occurring at the behavioral and&#xD;
        cerebral level, presenting a unique opportunity to detect prognostic brain-based biomarkers.&#xD;
        Likewise, improvements in electroencephalogram (EEG) neuroimaging strategies have led to&#xD;
        increased spatial resolution, enabling researchers to overcome the limitations of classically&#xD;
        used neuroimaging modalities (e.g., magnetic resonance imaging [MRI] and functional MRI),&#xD;
        such as high cost and limited accessibility. Using longitudinal EEG, this patient-oriented&#xD;
        research project will provide a comprehensive neural picture of emotional, cognitive, and&#xD;
        resting-state networks in patients with CLBP, which will aid in predicting symptom&#xD;
        progression in CLBP. Through this award, the investigators will use modern EEG source&#xD;
        analysis strategies to track biomarkers at baseline and 3- and 6-month follow-ups and their&#xD;
        covariance with markers for pain and emotional and cognitive dysfunction. In Aim 1, the&#xD;
        investigators will identify and characterize differences in resting-state, emotional, and&#xD;
        cognitive networks between patients with CLPB and age/sex-matched controls. In Aim 2, the&#xD;
        investigators will identify within-subject changes across time and their relationship with&#xD;
        clinical symptoms. In Aim 3, as an exploratory aim, the investigators will apply machine- and&#xD;
        deep-learning strategies to detect a comprehensive signature of CLBP using EEG features from&#xD;
        resting-state, emotional, and cognitive networks.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">December 2023</start_date>
    <completion_date type="Anticipated">February 2026</completion_date>
    <primary_completion_date type="Anticipated">February 2026</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>N/A</allocation>
      <intervention_model>Single Group Assignment</intervention_model>
      <intervention_model_description>All participants will complete all interventions.</intervention_model_description>
      <primary_purpose>Other</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Pain Intensity changes from baseline as assessed by the PROMIS current, 7 day maximal and 7 day average</measure>
      <time_frame>Baseline, 3-month and 6-month follow-ups</time_frame>
      <description>Within subjects change in pain intensity from baseline to each follow up point</description>
    </primary_outcome>
    <primary_outcome>
      <measure>EEG resting state functional connectivity changes from baseline</measure>
      <time_frame>Baseline, 3-month and 6-month follow-ups</time_frame>
      <description>Within subjects change in resting state functional connectivity from baseline to each follow up point.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>EEG late positive potential changes from baseline</measure>
      <time_frame>Baseline, 3-month and 6-month follow-ups</time_frame>
      <description>Within subjects change in late positive potential from baseline to each follow up point.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>EEG error related negativity changes from baseline</measure>
      <time_frame>Baseline, 3-month and 6-month follow-ups</time_frame>
      <description>Within subjects change in error related negativity from baseline to each follow up point.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Neuropsychological changes from baseline as assessed by the NIH toolbox</measure>
      <time_frame>Baseline, 3-month and 6-month follow-ups</time_frame>
      <description>Within subjects change in metrics of fluid and crystallized intelligence from baseline to each follow up point</description>
    </secondary_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">130</enrollment>
    <condition>Chronic Low-back Pain</condition>
    <condition>Healthy</condition>
    <arm_group>
      <arm_group_label>Single Arm</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>All participants will complete all interventions</description>
    </arm_group>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Resting State EEG</intervention_name>
      <description>During this intervention, participants will be asked to not think about anything in particular while EEG is recorded. Resting state will be conducted with either the participants having their eyes open, or eyes closed.</description>
      <arm_group_label>Single Arm</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Picture Viewing EEG</intervention_name>
      <description>During this intervention, participants will view emotionally charged pictures for a short period of time. Afterwards, participants will be asked to rate their emotional reactions to the pictures. EEG will be recorded during this intervention.</description>
      <arm_group_label>Single Arm</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Stop Signal EEG</intervention_name>
      <description>During this intervention, participants will be asked to respond quickly to a visual stimulus with a button press. At times, participants will be asked to inhibit their responses. EEG will be recorded during this intervention.</description>
      <arm_group_label>Single Arm</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Current diagnosis of Chronic Low Back Pain&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Current diagnosis of cancer&#xD;
  &#xD;
            -  Severe psychiatric conditions&#xD;
  &#xD;
            -  Pending personal litigation relating to an injury or receiving workers' compensation&#xD;
               benefits&#xD;
  &#xD;
            -  Being a non-English speaker.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>80 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 24, 2023</study_first_submitted>
    <study_first_submitted_qc>August 29, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 6, 2023</study_first_posted>
    <last_update_submitted>August 29, 2023</last_update_submitted>
    <last_update_submitted_qc>August 29, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 6, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Stanford University</investigator_affiliation>
      <investigator_full_name>Edward Lannon</investigator_full_name>
      <investigator_title>Pain Psychologist</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Back Pain</mesh_term>
      <mesh_term>Low Back Pain</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>The investigators will support requests for data sharing in a timely manner. Prior to sharing data, the data will be redacted of all personal identifiers to prevent subject identification.</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/NCT06025201
  org study id: 71778
  secondary id: 1K23AR083171-01
  nct id: NCT06025201
  lead sponsor: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  Chronic low back pain (CLBP) is a pervasive disorder affecting up to one-fifth of adults
        globally and is the single greatest cause of disability worldwide. Despite the high
        prevalence and detrimental impact of CLBP, its treatments and mechanisms remain largely
        unclear. Biomarkers that predict symptom progression in CLBP support precision-based
        treatments and ultimately aid in reducing suffering. Longitudinal brain-based resting-state
        neuroimaging of patients with CLBP has revealed neural networks that predict pain
        chronification and its symptom progression. Although early findings suggest that measurements
        of brain networks can lead to the development of prognostic biomarkers, the predictive
        ability of these models is strongest for short-term follow-up. Measurements of different
        neural systems may provide additional benefits with better predictive power.
  
        Emotional and cognitive dysfunction is common in CLBP, occurring at the behavioral and
        cerebral level, presenting a unique opportunity to detect prognostic brain-based biomarkers.
        Likewise, improvements in electroencephalogram (EEG) neuroimaging strategies have led to
        increased spatial resolution, enabling researchers to overcome the limitations of classically
        used neuroimaging modalities (e.g., magnetic resonance imaging [MRI] and functional MRI),
        such as high cost and limited accessibility. Using longitudinal EEG, this patient-oriented
        research project will provide a comprehensive neural picture of emotional, cognitive, and
        resting-state networks in patients with CLBP, which will aid in predicting symptom
        progression in CLBP. Through this award, the investigators will use modern EEG source
        analysis strategies to track biomarkers at baseline and 3- and 6-month follow-ups and their
        covariance with markers for pain and emotional and cognitive dysfunction. In Aim 1, the
        investigators will identify and characterize differences in resting-state, emotional, and
        cognitive networks between patients with CLPB and age/sex-matched controls. In Aim 2, the
        investigators will identify within-subject changes across time and their relationship with
        clinical symptoms. In Aim 3, as an exploratory aim, the investigators will apply machine- and
        deep-learning strategies to detect a comprehensive signature of CLBP using EEG features from
        resting-state, emotional, and cognitive networks.
  allocation: N/A
  intervention model: Single Group Assignment
  intervention model description: All participants will complete all interventions.
  primary purpose: Other
  masking: None (Open Label)
  measure: Pain Intensity changes from baseline as assessed by the PROMIS current, 7 day maximal and 7 day average
  time frame: Baseline, 3-month and 6-month follow-ups
  description: Within subjects change in pain intensity from baseline to each follow up point
  measure: EEG resting state functional connectivity changes from baseline
  time frame: Baseline, 3-month and 6-month follow-ups
  description: Within subjects change in resting state functional connectivity from baseline to each follow up point.
  measure: EEG late positive potential changes from baseline
  time frame: Baseline, 3-month and 6-month follow-ups
  description: Within subjects change in late positive potential from baseline to each follow up point.
  measure: EEG error related negativity changes from baseline
  time frame: Baseline, 3-month and 6-month follow-ups
  description: Within subjects change in error related negativity from baseline to each follow up point.
  measure: Neuropsychological changes from baseline as assessed by the NIH toolbox
  time frame: Baseline, 3-month and 6-month follow-ups
  description: Within subjects change in metrics of fluid and crystallized intelligence from baseline to each follow up point
  arm group label: Single Arm
  arm group type: Experimental
  description: All participants will complete all interventions
  intervention type: Behavioral
  intervention name: Resting State EEG
  description: During this intervention, participants will be asked to not think about anything in particular while EEG is recorded. Resting state will be conducted with either the participants having their eyes open, or eyes closed.
  arm group label: Single Arm
  intervention type: Behavioral
  intervention name: Picture Viewing EEG
  description: During this intervention, participants will view emotionally charged pictures for a short period of time. Afterwards, participants will be asked to rate their emotional reactions to the pictures. EEG will be recorded during this intervention.
  arm group label: Single Arm
  intervention type: Behavioral
  intervention name: Stop Signal EEG
  description: During this intervention, participants will be asked to respond quickly to a visual stimulus with a button press. At times, participants will be asked to inhibit their responses. EEG will be recorded during this intervention.
  arm group label: Single Arm
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 80 Years
  healthy volunteers: Accepts Healthy Volunteers
  responsible party type: Principal Investigator
  investigator affiliation: Stanford University
  investigator full name: Edward Lannon
  investigator title: Pain Psychologist
  mesh term: Back Pain
  mesh term: Low Back Pain
  sharing ipd: Yes
  ipd description: The investigators will support requests for data sharing in a timely manner. Prior to sharing data, the data will be redacted of all personal identifiers to prevent subject identification.