Exploration of the Pathophysiology of Chronic Pain Using Quantitative EEG Source Localization

• Published in: Clinical EEG and neuroscience 2018-03, Vol.49 (2), p.103-113
• Main Authors: Prichep, Leslie S., Shah, Jaini, Merkin, Henry, Hiesiger, Emile M.
• Format: Article
• Language: English
• Subjects: Analgesics; Brain; Brain research; Cerebral cortex; Chronic pain; EEG; Hemispheric laterality; Localization; Medical imaging; Mental depression; Neuroimaging; NMR; Nuclear magnetic resonance; Pain; Patients; Population; Prefrontal cortex; Public health; Regression analysis; Studies; Temporal cortex; Temporal resolution; Thalamus; Theta rhythms; Tomography
• ISSN: 1550-0594; 2169-5202
• DOI: 10.1177/1550059417736444

Chronic pain affects more than 35% of the US adult population representing a major public health imperative. Currently, there are no objective means for identifying the presence of pain, nor for quantifying pain severity. Through a better understanding of the pathophysiology of pain, objective indicators of pain might be forthcoming. Brain mechanisms mediating the painful state were imaged in this study, using source localization of the EEG. In a population of 77 chronic pain patients, significant overactivation of the “Pain Matrix” or pain network, was found in brain regions including, the anterior cingulate, anterior and posterior insula, parietal lobule, thalamus, S1, and dorsolateral prefrontal cortex (DLPFC), consistent with those reported with conventional functional imaging, and extended to include the mid and posterior cingulate, suggesting that the increased temporal resolution of electrophysiological measures may allow a more precise identification of the pain network. Significant differences between those who self-report high and low pain were reported for some of the regions of interest (ROIs), maximally on left hemisphere in the DLPFC, suggesting encoding of pain intensity occurs in a subset of pain network ROIs. 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. 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, the frequency specific (theta/low alpha band) overactivation in the regions reported, while not providing direct evidence, are consistent with a model of thalamocortical dysrhythmia as the potential mechanism of the neuropathic painful condition.