Functional Neuroimaging May Produce a Potential Objective Measure of Chronic Pain

Pain is a product of the brain and the experiences of pain can be shaped by mood, cognition, anxiety, fear, genetics, and other individual differences. Currently, pain is measured subjectively but an objective measure of pain may improve the diagnosis and management of patients with chronic pain.

During a morning plenary session at the 2013 American Academy of Pain Medicine annual meeting, held April 12-14 in Fort Lauderdale, FL, Sean Mackey, MD, PhD, of Stanford University Medical Center in San Francisco, CA, and Vitaly Napadow, PhD, of Harvard University Medical School in Boston, MA, provided insight into the use of functional neuroimaging as an objective measure of chronic pain..

During the session, Mackey discussed how patients respond differently to the same pain stimulus and how activation of regions of the brain can account for these differences, as well as genetics, sex, ethnicity, mental state, early life experiences, and environmental factors. In addition, the presence of fear and anxiety can worsen pain symptoms.

While subjective measures of pain can provide insight into the degree and extent of pain on an individualized basis, it does not provide insight into structural changes and/or potential biomarkers that may help to diagnose and improve chronic pain management. Studies have shown that structural changes in the brain occur in chronic pain patients as compared to healthy patients. In addition, neuroimaging has been used to predict changes in brain structure with treatment. Mackey discussed how a short course of opioids in chronic pain patients actually resulted in profound structural changes in the brain as compared to patients who did not receive opioids. In addition, neuroimaging revealed that these structural changes remained three months after discontinuation of treatment.

Mackey also discussed how other factors, including passionate love, could alter the same circuits involved in pain processing and perception. In addition, the presence of passionate love can help to reduce pain. In a recent study that Mackey and colleagues conducted, the investigators found that the presence of passionate love reduced pain intensity.

Napadow further expanded upon alterations in brain structure and function and explained how a neuroimaging biomarker may serve as an objective measure of chronic pain diagnosis and management. It is unlikely that neuroimaging would replace self-reported pain scales but may serve as an additional tool to help diagnose and potentially treat chronic pain conditions. There are positives and negatives to utilizing neuroimaging in clinical practice. On the positive side, the approach could optimize treatment and ,aid in drug development and diagnosis, as well as help in personalized medicine and improve insurance coverage and costs. On the negative side, it could lead to false negatives and denial of therapy as well as lead to false positives and unnecessarily treating patients that don’t require medication

Napadow further discussed the identification of biomarkers using fMRI and identified a correlation between the default mode network (DMN) and the insula and chronic pain. Recent studies have demonstrated a link between DMN and insula connectivity in patients with chronic pain but not in healthy patients. In addition, the greater the pain levels in a patient, the greater the connectivity between the DMN and insula. On the other hand, greater reductions in pain intensity lead to greater reductions in DMN and insula connectivity. While this is very exciting and may serve as a potential biomarker, whether a diagnostic or surrogate marker of pain, at this time it is too early to use this as a predictive marker. Currently, neuroimaging is not something to replace current standards of care.

Mackey concluded the session with a discussion on how an objective measure of pain is needed but it will not replace self reporting of pain. However, in a study evaluating fMRI as a predictive marker of pain, the investigators found an 88% overall accuracy in the prediction of pain, with good sensitivity, specificity, negative predictive value, and positive predictive value. The investigators also identified specific regions of the brain that may play a role in chronic pain, including the s2 region.