The perception of pain is very individual. Using EEG it may be possible to predict your threshold or experience of pain.
One day a seven-year-old girl walked into a hospital. She had a two-inch gash on her forehead, extending up toward her hairline. The injury required stitches. Rather than let the child suffer through each stitch, Dr. Christopher Stookey injected anesthetic around the wound before moving in with the stitches. The little girl was scared, but brave. She calmly chatted with the nurse while the doctor completed the procedure, never resisting or fussing at all. When the doctor had finished, he praised the little girl. Her response? “It didn’t hurt.”
This doesn’t seem unusual until it is compared to the man who Dr. Stookey was working with not long before. He, too, had come to the hospital with a two-inch gash on his forehead. He, too, had needed stitches and he, too, was first given anesthetic. He, however, was not nearly as calm and collected. He resisted and squirmed. He protested and complained. Ultimately, he needed to be held down by the nurse.
You may think that Dr. Stookey’s adult patient is just a whiny, overly-dramatic complainer. Even the nurse involved, a medical professional, was tempted to give him a lollipop as a jab for his comparatively poor behavior. To expect the two patients to react the same, however, would go against modern research into the very nature of pain.
The way people experience pain is not a set formula that can be applied to each individual. Instead, people experience pain very differently. A two on one person’s pain-scale could be a nine or ten on another person’s scale. Truly understanding this has led to huge breakthroughs in pain management techniques. For much of human history, physicians assumed that pain was purely physical.
It wasn’t until the 1950s that anyone began to look at pain as perception instead of a strictly physiological phenomenon. The researcher was William K. Livingstone and the idea was revolutionary. In his book Pain and Suffering, Livingstone grapples with an elusive, all-encompassing definition for pain. It is, he muses, both physical and psychological.
It was this direction of inquiry that eventually led to the development of the Gate Control Theory of Pain, which is still taught in many medical schools today. This theory posits a mechanism through which individualized perception of pain is possible. It involves two different fibers, one large and one small, that facilitate pain inputs and various cooperating mechanisms for the modulation of sensation.
And so it came to be that in 1968, Margo McCaffery famously defined pain as “whatever the experiencing person says it is, existing whenever he says it does.” Today health care providers take allegations of pain seriously. Though this is not always put into practice perfectly, we have come a long way in recognizing and respecting the individualized nature of pain perception. New research further backs this practice and its theoretical basis.
Chief among them is research that uses new technologies to add physical proof to our theoretical understanding of pain – specifically experiments involving the electroencephalogram (EEG), which measures aggregate electrical activity (Read more about the EEG signal here and its discovery here). With the increasing ability to measure EEG affordably and portably (Read Taking Neurotechnology out of the Lab) it is a faster and easier technology to work with than FMRI (see 7 Ways to peer into the living human brain for an overview of technologies).
In 2012, a collaborative groups of scientists from the United States and Germany used the EEG to show electrical differences between people who report being sensitive to pain, and those who report being less sensitive. In the study, researchers delivered an identical series of painful laser-based stimuli to consenting adults. For each stimulus experienced, participants were asked to rate the pain on a scale of one to ten. Every adult was exposed to exactly the same stimulation in exactly the same order, but they reported varying levels of pain. By analyzing spectral features of the EEG using machine learning (SVM to be specific), researchers were able to predict which participants had a high pain threshold (>5 on a pain scale) versus those with a low threshold (< or =5) with an accuracy of 83 percent.
Like other similar studies using FMRI and other measurements, this study suggests that our experience of pain reflects in the macro features of our brain activity and lends credence to the theory that people really do experience pain differently. While machine learning may not give us insights into the mechanism itself, it nonetheless provides a physiological prediction of its experience. People suffering from chronic pain have long been doubted and even dubbed drug addicts. With physical proof of the pain they describe, this callous delineation should finally end for good. The study also bolsters arguments in support of individualized pain management systems. A person who experiences pain at a low level needs to be treated differently from a person who experiences it at a high level or those who experience chronic pain.
This leaves us with one final question: What’s next? Developing the tools to use this information in a medical setting and beginning the journey towards personalized medicine. Some companies are already releasing relevant products. PainQx is one such company. Their EEG device is specifically designed to objectively measure pain, providing an additional tool to clinicians worldwide. This may be especially useful to non-verbal patients. Disabled people, those in comas, or people who are unconscious cannot tell us how they feel. Perhaps soon they won’t need to.