fMRI Brain Scan Patterns Provide First Impartial Gauge of Pain

Scientists for the first time have been able to forecast how much pain individuals are feeling by just looking at images of their brains.

The study, published on April 10, 2013, in the New England Journal of Medicine (NEJM), may lead to the development of effective ways that physicians can use to objectively quantify a patient’s pain. Currently, pain intensity can only be gauged based on a patient’s own description, which often includes rating the pain on a scale of one to 10. Objective measures of pain could confirm these pain reports and provide new insights into how the brain generates different types of pain.

The new findings also may provide the opportunity for the development of technology utilizing functional magnetic resonance imaging (fMRI) brain scans to objectively measure anger, anxiety, depression, or other emotional states. “Right now, there’s no clinically acceptable way to measure pain and other emotions other than to ask a person how they feel,” said Dr. Tor Wager, associate professor of psychology and neuroscience at University of Colorado Boulder (CU-Boulder; USA), and lead author of the paper.

The researchers, which included scientists from New York University (New York, NY, USA), Johns Hopkins University (Baltimore, MD, USA), and the University of Michigan, used computer data-mining techniques to sift through images of 114 brains that were captured when the study participants were exposed to multiple levels of heat, ranging from benignly warm to painfully hot. Utilizing the computer, the scientists identified a distinctive neurologic signature for the pain. “We found a pattern across multiple systems in the brain that is diagnostic of how much pain people feel in response to painful heat.” Dr. Wager said.

The researchers anticipated, conducting the research, that if a pain signature could be seen it would probably be distinctive to each individual. If that were the case, an individual’s pain level could only be predicted based on past images of his or her own brain. Instead, they found that the signature was transferable across different individuals, allowing the scientists to predict how much pain a person was being caused by the applied heat, with between 90%–100% accuracy, even with no earlier brain scans of that individual to use as a reference point.

The scientists also were startled to find that the signature was specific to physical pain. Earlier research has shown that social pain can appear very similar to physical pain in the way the brain activity it produces. One study, for example, revealed that the brain activity of people who have just been through a relationship breakup, and who were shown an image of the individual who rejected them, is similar to the brain activity of someone feeling physical pain.

However, when the investigators tested to see if the newly defined neurologic signature for heat pain would also pop up in the data collected earlier from the heartbroken participants, they found that the signature was absent. Ultimately, the scientists set out to see if the neurologic signature could detect when an analgesic was used to lessen the pain. The findings showed that the signature recorded a decrease in pain in subjects administered a painkiller.

The study’s findings revealed that the investigators cannot quantify physical pain, but they lay the potential for future research that could generate the first objective pain evaluations by clinicians and hospitals. To accomplish this, Dr. Wager and his colleagues are already examining how the neurologic signature can be verified when applied to different types of pain.

“I think there are many ways to extend this study, and we’re looking to test the patterns that we’ve developed for predicting pain across different conditions,” Dr. Wager said. “Is the predictive signature different if you experience pressure pain or mechanical pain, or pain on different parts of the body? We’re also looking towards using these same techniques to develop measures for chronic pain. The pattern we have found is not a measure of chronic pain, but we think it may be an ‘ingredient’ of chronic pain under some circumstances. Understanding the different contributions of different systems to chronic pain and other forms of suffering is an important step towards understanding and alleviating human suffering.”

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University of Colorado Boulder
New York University
Johns Hopkins University