NCT06025201 · Stanford University
Detection of EEG-Based Biomarkers of Chronic Low Back Pain
What this study is about
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.
View original scientific description
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 1- and 2-month follow-ups and their covariance with markers for pain and emotional and cognitive dysfunction. A 5-month follow up will also be used to only assess patient reported outcomes. 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.
Interventions
BEHAVIORAL
Resting State EEG
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.
BEHAVIORAL
Picture Viewing EEG
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.
BEHAVIORAL
Stop Signal EEG
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.
Primary outcome measures
Pain Intensity changes from baseline as assessed by the PROMIS current, 7 day maximal and 7 day average
Time frame: Baseline, 1-month, 2-month and 5-month follow-ups
Within subjects change in pain intensity from baseline to each follow up point. Pain intensity measures are 0-10 range with larger numbers indicating more pain
EEG resting state functional connectivity changes from baseline
Time frame: Baseline, 1-month and 2-month follow-ups
Within subjects change in resting state functional connectivity from baseline to each follow up point.
EEG late positive potential changes from baseline
Time frame: Baseline, 1-month and 2-month follow-ups
Within subjects change in late positive potential from baseline to each follow up point.
EEG error related negativity changes from baseline
Time frame: Baseline, 1-month and 2-month follow-ups
Within subjects change in error related negativity from baseline to each follow up point.
Who can participate
This study lists these criteria on ClinicalTrials.gov. A study coordinator reviews eligibility during screening — this page does not determine whether you qualify.
Inclusion criteria
- Current diagnosis of Chronic Low Back Pain
Exclusion criteria
- Current diagnosis of cancer
- Severe psychiatric conditions
- Pending personal litigation relating to an injury or receiving workers' compensation benefits
- Being a non-English speaker.
Where
- Palo Alto, California
Collaborators
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
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Frequently asked questions
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Data: ClinicalTrials.gov · synced May 15, 2026 · Source of record for eligibility and locations