LAS VEGAS—Clinicians currently rely on self-report of pain—the gold standard—but such reporting is limiting, with respect to individualized effective treatments, said Sean Mackey, MD, PhD, Director of the Stanford Systems Neuroscience and Pain Lab, Stanford, California.
This is especially true for vulnerable populations, including the very young, older patients with dementia, and ICU patients as well as the thousands of cases each year based on pain and suffering due to personal injury, and worker’s compensation, added Dr. Mackey, who is also Redlich Professor and Chief, Pain Medicine Division at Stanford.
For these reasons, there is a need for objective biomarkers of pain. He predicted the future of neuroimaging brain-based biomarkers would include those categorized as diagnostic, predictive, and prognostic as well as those that would stratify drug response, efficacy, and toxicity and serve as a surrogate endpoint.
One aspect of pain is its high individual variability among patients. “What is ’10 out of 10′ pain?” Dr. Mackey asked. “Why is there such wide variability in pain to a given stimulus or injury? Analgesic or treatment response to a given therapy? Amount of disease burden for a given injury? Development of chronic pain after an acute injury?”
One answer may be genetics. To date, more than 300 mice genes have been found to modulate pain sensitivity and analgesia, and more than 20 humans SNPs have been discovered. Genes are believed to account for 20% to 60% of variability of pain, with women slightly (8%) more sensitive to heat pain. Ethnicity and anxiety, catastrophizing, and somatization can also affect pain variability.
Also addressed was neural plasticity and clinical pain conditions and pain therapies and their effect on the brain. For example, distraction has been shown to reduce brain activity during pain, so recommending to a patient to “walk the dog, read a book, or go back to work” will be helpful unless the pain is greater than 7 on a scale of 1 to 10, when “pain overwhelms the system.”
Brief mindfulness meditation training has also been found to modulate brain systems. For example, Zeidan et al found 4 days of mindfulness-based meditation following a thermal stimulus of 49°C reduced pain processing in S1. Pain intensity, reduced by 40%, was associated with increased cingulate and right anterior insula activity. Pain unpleasantness decreased 57% and was associated with increased orbitofrontal and decreased thalamic activity
He described current research to use machine learning approaches, or “Big Data” to detect and characterize pain from brain data objectively, or “what’s going on when pain goes bad.” Pattern classification of pain is currently being used with real-time fMRI to modulate brain patterns and systems directly; however, “we’re not ready to turn this into a pain scanner,” he said, concluding with anticipated future directions for neuroimaging.