EEG Biomarkers for Chronic Lower Back Pain

Overseen By Omar Altirkawi

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Stanford University

Disqualifiers: Cancer, Severe psychiatric, Litigation, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 4

130 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to better understand chronic lower back pain by studying brain activity using EEG, a test that detects electrical activity in the brain. Researchers will examine brain patterns related to emotions and thinking to predict changes in symptoms over time. Participants will undergo EEG tests both at rest and during specific tasks to gather this information. Those with a current diagnosis of chronic low back pain may join, but individuals with cancer or severe psychiatric issues cannot participate. As an unphased trial, this study offers participants the chance to contribute to groundbreaking research that could lead to new insights and treatments for chronic pain.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that these EEG techniques are safe for studying chronic lower back pain?

Research has shown that using EEG (a test that records brain activity) in studies on chronic pain, such as chronic lower back pain, is generally safe and well-tolerated. Different types of EEG tests, including Picture Viewing EEG, Resting State EEG, and Stop Signal EEG, have demonstrated positive results without serious safety concerns.

In a large study with 614 participants, Picture Viewing EEG did not cause any severe side effects, indicating it is a safe method for studying brain activity in people with chronic pain.

Several studies have reviewed Resting State EEG and found it helpful for diagnosing chronic pain without significant side effects. This method involves monitoring brain activity while participants are relaxed and is generally considered safe.

The Stop Signal EEG was tested in a controlled study and found to be safe and acceptable, with no major safety issues reported.

Overall, research supports the safety of using EEG to study chronic pain, including chronic lower back pain. These findings suggest that participants in EEG studies can expect a safe and comfortable experience. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the EEG techniques being explored for chronic lower back pain because these methods aim to provide a deeper understanding of brain activity related to pain. Unlike standard treatments like medication or physical therapy, which primarily address symptoms, these EEG approaches—Picture Viewing EEG, Resting State EEG, and Stop Signal EEG—focus on identifying specific brain activity patterns linked to pain. By doing so, they could lead to more personalized and effective treatment plans in the future, potentially offering insights that current options cannot.

What evidence suggests that these EEG methods are effective for predicting chronic lower back pain progression?

Research has shown that EEG, a test measuring brain activity, can help predict chronic low back pain and its symptoms. Studies have found that EEG can detect differences in brain activity between individuals with chronic low back pain and those without it. For example, changes in brain activity and connections have been observed in specific brain areas of those with chronic low back pain. One study found that specific EEG features, such as peak alpha frequency, might indicate a person's sensitivity to pain. These findings suggest that EEG could be a valuable tool for understanding and managing chronic low back pain by identifying key brain patterns. In this trial, participants will complete interventions involving Picture Viewing EEG, Resting State EEG, and Stop Signal EEG to further explore these biomarkers. ^{2 6 7 8 9}

Are You a Good Fit for This Trial?

This trial is for adults with chronic low back pain (CLBP) who can speak English. It's not suitable for individuals with cancer, severe psychiatric conditions, those involved in injury litigation or receiving workers' compensation benefits.

Inclusion Criteria

I have been diagnosed with chronic low back pain.

Exclusion Criteria

Severe psychiatric conditions

Pending personal litigation relating to an injury or receiving workers' compensation benefits

Being a non-English speaker.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Assessment

Baseline EEG and neuropsychological assessments are conducted

1 week

1 visit (in-person)

Follow-up

Participants undergo EEG and neuropsychological assessments at 1-month and 2-month follow-ups

2 months

2 visits (in-person)

Extended Follow-up

Participants are assessed for patient-reported outcomes at a 5-month follow-up

3 months

1 visit (in-person)

What Are the Treatments Tested in This Trial?

Interventions

• Picture Viewing EEG
• Resting State EEG
• Stop Signal EEG

Trial Overview The study tests if EEG-based biomarkers can predict the progression of CLBP symptoms. Participants will undergo EEGs to track brain activity related to resting state, emotions, and cognition at baseline and then after 3 and 6 months.

How Is the Trial Designed?

Group I: Single Arm Experimental Treatment 3 Interventions

All participants will complete all interventions

Who Is Running the Clinical Trial?

Stanford University

Lead Sponsor

Trials

2,527

Recruited

17,430,000+

National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

Collaborator

Trials

508

Recruited

1,090,000+

Published Research Related to This Trial

This study involving 27 subjects with chronic low back pain found that changes in alpha oscillations of the brain, specifically in the central region, are negatively correlated with perceived pain intensity, suggesting a potential biomarker for pain assessment.

The strongest correlation was observed at the Cz electrode, indicating that measuring alpha power differences between eyes-closed and eyes-open conditions could provide a quantitative method for evaluating chronic pain in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Low Back Pain Assessment Based on Alpha Oscillation Changes in Spontaneous Electroencephalogram (EEG). Feng, L., Li, H., Cui, H., et al.[2022]

This systematic review analyzed 26 studies on musculoskeletal pain, revealing a trend of decreased alpha and beta EEG power in chronic pain conditions, while resting-state EEG showed increased theta and alpha power in chronic musculoskeletal pain.

The evidence for EEG changes in chronic and experimentally induced musculoskeletal pain is considered 'very low,' indicating that more high-quality research is needed to clarify these EEG characteristics and their implications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Is There a Difference in EEG Characteristics in Acute, Chronic, and Experimentally Induced Musculoskeletal Pain States? a Systematic Review. Mathew, J., Perez, TM., Adhia, DB., et al.[2023]

Individuals with chronic pain exhibit increased theta and alpha EEG power at rest, along with decreased amplitude of evoked potentials during sensory stimulation and cognitive tasks, indicating altered brain activity associated with pain.

Quantitative Electroencephalography (qEEG) may serve as a valuable and objective tool for understanding the brain mechanisms of chronic pain and could be useful for measuring outcomes in therapeutic studies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electroencephalographic Patterns in Chronic Pain: A Systematic Review of the Literature. Pinheiro, ES., de Queirós, FC., Montoya, P., et al.[2022]

In a study involving 50 chronic low back pain (cLBP) patients and 44 healthy controls, abnormal functional connectivity (FC) in specific brain regions was identified, particularly in the medial prefrontal cortex and rostral anterior cingulate cortex, which correlated with pain duration and severity.

The study demonstrated that resting-state functional connectivity could accurately distinguish cLBP patients from healthy controls with 91% accuracy in the initial cohort, suggesting that these brain connectivity patterns may play a crucial role in understanding and managing chronic pain.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Abnormal medial prefrontal cortex functional connectivity and its association with clinical symptoms in chronic low back pain. Tu, Y., Jung, M., Gollub, RL., et al.[2022]

A study involving 57 adults with chronic low back pain found that psychological treatments like cognitive therapy, mindfulness meditation, and mindfulness-based cognitive therapy significantly reduced theta and alpha brain wave activity, indicating changes in brain function associated with pain management.

All treatments led to a reduction in beta power across multiple brain regions, with a specific link between reduced beta power in the central region and decreased pain intensity observed only in mindfulness-based cognitive therapy, suggesting that these psychological approaches can effectively alter brain activity related to pain perception.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Change in Brain Oscillations as a Mechanism of Mindfulness-Meditation, Cognitive Therapy, and Mindfulness-Based Cognitive Therapy for Chronic Low Back Pain. Day, MA., Matthews, N., Mattingley, JB., et al.[2022]

In a study involving 14 healthy participants, the experience of acutely induced low back pain (LBP) significantly altered their postural coordination and muscle activation during sit-to-stand movements, leading to longer movement times and decreased force exerted under the feet.

The study found that changes in cerebrocortical activity, specifically the contingent negative variation, correlated with the participants' movement modifications during and after experiencing LBP, suggesting a neural mechanism that could be targeted in future research on LBP-related movement patterns.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effects of experimentally induced low back pain on the sit-to-stand movement and electroencephalographic contingent negative variation. Jacobs, JV., Yaguchi, C., Kaida, C., et al.[2021]

The study involving 14 patients with chronic low back pain showed that ankle acupuncture (AA) significantly reduced pain intensity, as measured by Visual Analog Scale scores, after treatment (P<0.001).

Brain imaging revealed changes in low-frequency brain activity, specifically a decrease in the left insular and increases in the left precuneus and right precentral gyrus, indicating that AA may provide immediate pain relief through specific brain activity modulation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The low-frequency BOLD signal oscillation response in the insular associated to immediate analgesia of ankle acupuncture in patients with chronic low back pain. Xiang, A., Yu, Y., Jia, X., et al.[2022]

In a study of 95 subjects over 6 months, those who developed chronic pain showed decreased alpha power in their EEG readings, indicating a less relaxed brain state, while those who became pain-free exhibited increased alpha power.

These findings suggest that changes in baseline EEG activity are associated with transitions in pain states, which could inform future treatments like neurofeedback for chronic pain management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Does baseline EEG activity differ in the transition to or from a chronic pain state? A longitudinal study. Schuurman, BB., Vossen, CJ., van Amelsvoort, TAMJ., et al.[2023]

This systematic review of 12 studies on low back pain (LBP) found evidence of altered brain function, particularly in balance control and decision-making processes, suggesting that chronic LBP may require more cortical attention and affect sensory processing of pain.

Moderate evidence indicated that while some aspects of sensory processing are altered in LBP, the emotional processing of pain stimuli appears unaffected, highlighting the complexity of brain responses in individuals with chronic low back pain.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Are Functional Brain Alterations Present in Low Back Pain? A Systematic Review of EEG Studies. Schouppe, S., Van Oosterwijck, S., Danneels, L., et al.[2021]

This study identified specific brain connectivity patterns as potential noninvasive biomarkers for chronic low back pain (LBP), achieving an impressive classification accuracy of 83.1% in distinguishing LBP patients from healthy controls based on brain imaging data.

The research introduced a novel feature selection method called Enet-subset, which effectively improved the performance of machine learning classifiers by reducing redundant variables, highlighting its potential for enhancing diagnostic tools in chronic pain assessment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability. Lamichhane, B., Jayasekera, D., Jakes, R., et al.[2021]

Chronic low back pain (LBP) is associated with significant changes in brain structure and connectivity, as shown by differences in cortical thickness and resting-state functional connectivity in a study of 24 LBP patients compared to 27 healthy controls.

Machine learning analysis of cortical thickness data achieved a classification accuracy of 74.51%, suggesting that these brain changes could serve as biomarkers to help guide more effective treatments for LBP.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multi-modal biomarkers of low back pain: A machine learning approach. Lamichhane, B., Jayasekera, D., Jakes, R., et al.[2021]

Patients with chronic low back pain (cLBP) showed altered brain resting state functional connectivity (FC) in the primary somatosensory cortex (S1), with decreased FC during low-intensity pain and increased FC during high-intensity pain, indicating a dynamic response to pain levels.

Additionally, cLBP patients exhibited increased cortical thickness in areas of S1 associated with the lower back compared to healthy controls, suggesting structural plasticity in the brain related to chronic pain.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

S1 is associated with chronic low back pain: a functional and structural MRI study. Kong, J., Spaeth, RB., Wey, HY., et al.[2021]

Citations

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Machine learning classification of active viewing of pain and ... The present study explored whether active viewing, ie, requiring participant engagement in a task, of neutral and pain stimuli improves ML performance.

2. sciencedirect.com sciencedirect.com/science/article/pii/S2352396425003998

Exploring electroencephalographic chronic pain biomarkers The study included eight datasets, each comprising resting-state EEG and metadata of people with different types of chronic pain. To ensure accurate EEG feature ...

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Systematic Review of the Effectiveness of Machine ... This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatment response from EEG data.

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The Analysis of Spontaneous Electroencephalogram (EEG) in ... The present study aimed to assess chronic low back pain (CLBP) based on functional brain changes with EEG in CLBP patients compared with healthy controls.

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a double-blinded safety and feasibility randomized placebo ... Chronic low back pain (CLBP) is a disabling condition worldwide. In CLBP, neuroimaging studies demonstrate abnormal activities in cortical ...

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In search of a composite biomarker for chronic pain by way of ... This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain.

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A safety and feasibility randomized placebo controlled trial ... This study evaluates the safety, feasibility, and acceptability of a novel electroencephalography-based neurofeedback training, targeting effective alpha-band ...

8. onlinelibrary.wiley.com onlinelibrary.wiley.com/doi/10.1155/2021/8537437

Low Back Pain Assessment Based on Alpha Oscillation ... This study is aimed at assessing chronic low back pain based on alpha oscillations. Multichannel electroencephalogram data were recorded from 27 subjects.

9. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9860016/

Exploring electroencephalographic infraslow neurofeedback ... Exploring electroencephalographic infraslow neurofeedback treatment for chronic low back pain: a double-blinded safety and feasibility ...