Is resting state eeg a good biomarker for chronic pain?
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Resting-state EEG shows promise as a potential biomarker for chronic pain. Studies have highlighted alterations in brain oscillations and connectivity patterns in chronic pain patients compared to healthy controls . Advanced feature selection algorithms have been proposed to enhance the accuracy of chronic pain detection using resting-state EEG data, achieving high test accuracies . A systematic review of EEG studies in chronic pain patients revealed higher theta and beta power compared to healthy individuals, suggesting potential diagnostic biomarker utility . Despite heterogeneous results, resting-state EEG holds potential for objectively assessing chronic pain, aiding in treatment strategies and potentially guiding future research on pain biomarkers.
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3 Citations | Resting state EEG shows potential as a diagnostic biomarker for chronic pain, with higher theta and beta power observed in patients compared to healthy individuals, as per the systematic review. |
| Resting-state raw EEG signals, with improved feature selection using mSFFS, are effective for chronic pain detection, achieving high test accuracies of 97.5% and 81.4% on different datasets. | |
27 Jun 2023 | Resting-state raw EEG signals, with improved feature selection, are effective for chronic pain detection, achieving high test accuracies of 97.5% and 81.4% on different datasets. |
| Resting-state EEG patterns are investigated as potential neural markers for chronic pain before Spinal Cord Stimulation surgery, suggesting EEG may serve as a biomarker for chronic pain. | |
07 Apr 2023 | Resting-state EEG connectivity, particularly in the insula, serves as a potential biomarker for chronic pain processing and symptom severity in fibromyalgia, indicating its relevance in assessing pain conditions. |
Related Questions
What are the potential clinical applications of EEG network studies in the treatment and management of chronic pain conditions?10 answersElectroencephalography (EEG) network studies hold significant potential in advancing the treatment and management of chronic pain conditions through various clinical applications. One of the primary applications is the identification of brain-based biomarkers for chronic pain, which can inform targeted treatments. For instance, alterations in the Alpha and Theta frequency bands have been identified as potential underlying abnormalities in chronic pain conditions such as Fibromyalgia, suggesting a role in central pain mechanisms. Similarly, enhanced connectivity within the frontoparietal network in the Theta band has been observed in patients with chronic primary pain (CPP), indicating hyperexcitability in attentional control. These findings underscore the utility of EEG in uncovering neural characteristics of chronic pain.
Moreover, EEG network studies can aid in the selection of Spinal Cord Stimulation (SCS) parameters by correlating EEG activity with the incidence and severity of pain, thus moving beyond subjective patient reports. The development of machine learning models, particularly deep learning classifiers like convolutional neural networks (CNNs), has further enhanced the predictive potential of EEG data in identifying pain states with high accuracy, offering a pathway for dynamic therapy modification and rehabilitation tracking in chronic pain patients.
Additionally, EEG network studies have demonstrated the potential for identifying functional connectivity biomarkers in conditions like Failed Back Surgery Syndrome (FBSS), which could guide the application of SCS treatments. The use of advanced classification methods, such as support vector machine (SVM) and logistic regression models, has shown promise in detecting subjective pain experiences from EEG data, paving the way for the development of point-of-care systems for pain detection in populations unable to self-report.
Finally, the application of deep CNNs in EEG studies has been explored for distinguishing between induced pain states and resting states in chronic back pain patients, offering robust performance in pain detection that could benefit clinical practice. Collectively, these applications highlight the transformative potential of EEG network studies in enhancing the understanding, treatment, and management of chronic pain conditions.
Is resting state EEG a good biomarker for chronic pain?5 answersResting-state EEG shows promise as a potential biomarker for chronic pain. Studies have highlighted alterations in brain oscillations and connectivity patterns in chronic pain patients compared to healthy controls. Advanced feature selection algorithms have been proposed to enhance the accuracy of chronic pain detection using resting-state EEG data. A systematic review of EEG studies in chronic pain patients revealed higher theta and beta power compared to healthy individuals, suggesting potential diagnostic biomarker utility. Despite some inconsistencies in results, resting-state EEG holds potential for objectively assessing chronic pain. Further research and refinement in methodologies could establish resting-state EEG as a valuable tool for understanding and characterizing chronic pain mechanisms.
Meditation effective for chronic pain management?5 answersMeditation, particularly mindfulness meditation, shows promise for the management of chronic pain. Studies have found that meditation can have a bigger impact on the emotional response to pain rather than the reduction in pain intensity. Home practice of meditation skills is an important component of "third wave" psychotherapies for chronic pain, and studies have shown positive health outcomes associated with the amount of practice. Multi-component yoga interventions, including meditation-based lifestyle modification, have been found to be effective in reducing pain intensity, improving quality of life, and increasing pain self-efficacy in patients with chronic pain. Mindfulness-based pain management programs have also shown significant positive changes in patients with chronic pain, with non-fibromyalgia chronic pain patients benefiting more than those with fibromyalgia. Traditional Japanese Buddhism meditation-integrated cognitive behavioral therapy has been found to be effective in reducing subjective pain and improving brain function in chronic low back pain patients.
Does MRI imaging of chronic pain patients show any changes in sleep patterns?5 answersMRI imaging of chronic pain patients has shown changes in sleep patterns. Polysomnographic findings in patients with chronic musculoskeletal pain revealed poor sleep quality, nocturnal awakenings, and specific disturbances in sleep phases. In patients with chronic low back pain, MRI changes were noted in over 95% of the cases, with degenerative changes being the most common finding. Objective polysomnography data demonstrated changes in the structure of sleep in chronic pain syndrome, specifically chronic back pain, with presomnic, intrasomnic, and postsomnic disorders being detected. Additionally, patients taking opioid analgesia for chronic back pain showed significant sleep and wake disturbances, as well as abnormal brain activity during sleep. These findings suggest that MRI imaging can reveal changes in sleep patterns in chronic pain patients, highlighting the importance of assessing sleep quality in this population.
What is the effect of pain on eeg?5 answersPain has been shown to have an effect on EEG activity. One study found that pain-evoked EEG responses are significantly correlated with an individual's spontaneous EEG, and proposed a normalization method using one's spontaneous EEG to reduce inter-individual variability in pain-evoked responses. Another study used EEG signals to detect the presence of pain in healthy subjects and found significant results in the beta frequency band for detecting pain. Additionally, a study on pain during electromyography (EMG) found that pain perception was decreased in women who received written material before the test, but not in men. Another study investigated the use of EEG as an index for self-controlling acute pain and found changes in alpha and beta frequencies during acute pain. Finally, a study on repetitive tonic pain found decreases in alpha-1 and -2 activities during muscle pain, as well as an increase in beta-2 activity.
What is the effect of pain on eeg?publications since 2018?3 answersPain has been found to have an effect on EEG activity. Several studies have investigated this relationship. One study found that subjective pain sensation was associated with changes in EEG activity in multiple frequency bands, including alpha2, beta1, beta2, and theta. Another study found that musculoskeletal pain altered the EEG signal, particularly in the alpha and beta bands, during movement. Chronic pain patients were found to have abnormal EEG activity under negative mood conditions, as well as differences in somatosensory event-related potentials and EEG band power. Pain perception during electromyography was decreased when patients received written information before the test. In patients with sickle cell disease, increased theta power and decreased beta2 power were observed in the EEG, with areas of greater theta activity related to pain processing. These findings suggest that pain can influence EEG activity and that EEG measures may be useful in understanding and assessing pain.