Exploration of the Pathophysiology of Chronic Pain Using Quantitative EEG Source Localization
Findings support the potential to derive a quantitative measure of the severity of pain using information extracted from a multivariate descriptor of the abnormal overactivation. Furthermore, a preliminary multivariate logistic regression analysis was used to select quantitative-EEG features which demonstrated a highly significant predictive relationship of self-reported pain scores.
Evaluation of the pain matrix using EEG source localization: a feasibility study.
The areas that were activated in the high pain state localized to the same regions reported by other neuroimaging methods and with frequency specificity. The frequency and regionally specific activation may indicate distinctive patterns of pathophysiology underlying the pain matrix. Although in a small number of patients, this work suggests that QEEG may be a useful tool in the exploration and quantification of the pain matrix in a clinical setting.
Brain Imaging of Pain: State of the Art
Advances made in neuroimaging have bridged the gap between brain activity and the subjective experience of pain and allowed us to better understand the changes in the brain that are associated with both acute and chronic pain. This article provides an overview of neural imaging techniques that have been used to quantify pain.
Review: Electroencephalographic Patterns in ChronicPain
This review suggests that qEEG could be considered as a simple and objective tool for the study of brain mechanisms involved in chronic pain, as well as for identifying the specific characteristics of chronic pain condition. In addition, results show that qEEG probably is a relevant outcome measure for assessing changes in therapeutic studies.
Review: Electroencephalography and Analgesics
This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients