Stanford scientists work towards developing a “painometer”

 By Tracie White on September 13th, 2011


About two years ago, Stanford neurologist Sean Mackey, MD, PhD, was asked by defense lawyers in a workman’s compensation case to serve as an expert witness. A man, burned by chemicals at work, wanted compensation from his employer for chronic pain, and his attorney was attempting to use brain scanning evidence to prove that his client was in chronic pain. Functional magnetic resonance imaging scans of his brain showed heightened activity in a network of regions associated with pain. But the question was, did this prove he was in pain?
According to Mackey, definitely not. The case was settled out of court.
“I was very critical of the findings,” Mackey recently told me. “In fact, they had not proven that this person had chronic pain. He may well have been in chronic pain, but current technology could not determine this.”
That experience helped spark Mackey’s interest in working toward finding technology that could someday achieve such a goal. Now, a study based on work from Mackey’s lab has taken a first step toward the development of a diagnostic tool that would use patterns of brain activity to give an objective physiologic assessment of whether someone is in pain.
The press release I wrote about the study, which was published online in PLoS One today, specifies that this is preliminary research and that much more needs to be done before the creation of a usable “painometer.” But early results are promising:

Researchers took eight subjects, and put them in the brain-scanning machine. A heat probe was then applied to their forearms, causing moderate pain. The brain patterns both with and without pain were then recorded and interpreted by advanced computer algorithms to create a model of what pain looks like. The process was repeated with a second group of eight subjects.
The idea was to train a linear support vector machine — a computer algorithm invented in 1995 — on one set of individuals, and then use that computer model to accurately classify pain in a completely new set of individuals.
The computer was then asked to consider the brain scans of eight new subjects and determine whether they had thermal pain.
“We asked the computer to come up with what it thinks pain looks like,” co-author Neil Chatterjee said. “Then we could measure how well the computer did.” And it did amazingly well. The computer was successful 81 percent of the time.

Such a tool, which could possibly be useful someday in a court of law, has long been sought after by physicians, Mackey told me. The current method of “self-reporting” – when doctors ask patients to rank their pain on a scale of 1-to-10 – is limiting, he said. Too many patients, especially the very young and the very old, have difficulty communicating pain. “Wouldn’t it be great if we had a technique that could measure pain physiologically?” he asked.
Previously: Using philosophy to create a vocabulary of pain, No pain, no gain. Not!, Relieving Pain in America: A new report from the Institute of Medicine, Stanford’s Sean Mackey discusses recent advances in pain research and treatment and Oh what a pain
Photo by El Gran Dee