If you've ever visited the emergency department with appendicitis, or you're one of the 100 million U.S. adults who suffer from chronic pain, you're familiar with a row of numbered faces, with expressions from smiling to grimacing, used to indicate pain levels.
Despite that tool's widespread use, some researchers say a more empirical approach would better serve both patients and the physicians who provide care.
"Sadly, this scale of smiley faces, called the visual analogue scale, is the gold-standard pain-assessment tool," said the senior author. "Our goal is to associate specific brain activity with various scores on the numerical scale to make pain assessment more objective. We want to help patients with chronic pain and their physicians get into agreement about pain level so it is better managed and diagnosed, which may reduce the over-prescription of opioids."
Researchers have developed an electroencephalography-based test to objectively measure pain. Electroencephalography (EEG) is a method that measures brain activity using electrodes placed on the scalp. The brain activity is measured in the form of oscillations or "waves" of a certain frequency, somewhat like the specific frequency that dictates a radio station.
A frequency that correlates with pain in animals is called the "theta band," the senior author said. Computational analysis of theta brain waves to determine their power can be used to objectively measure pain in rodents and humans in a non-invasive manner, the author added.
In a paper published in Scientific Reports, the team reported that measuring the power of theta waves using EEG is an effective and direct test of pain and potential pain medication efficacy in pre-clinical animal models.
The current method to measure pain, and the effectiveness of potential pain medications, in a pre-clinical animal model is to poke the animal's paw and see how quickly it moves its paw away. Slow paw withdrawal is linked to less pain and better pain medication. Faster paw withdrawal is linked to more pain and less effective pain medication.
"When I was a graduate student, I hated this test because it had nothing to do with clinical pain," said the senior author. "Nobody pokes a patient with back pain. I'm just so happy that I beat this test, now we're working with something better."
Since the EEG-based test is a more direct measure of ongoing, spontaneous pain than the current approach, it could help researchers develop more effective medications for chronic back pain or sciatica, which don't have many effective treatments, the senior author said.
In the paper, the team looked at three pain medications and compared their effectiveness in an animal model of sciatica. The researchers used the traditional behavior test, the EEG test and an analysis to determine blood concentration of the medications, which was compared with the clinical blood concentration of the medications in human patients.
The first medication they tested was a proven treatment for some forms of chronic pain, which is sold under the brand name Lyrica. The second was a promising pain medication in phase two clinical trials, and the third was a medication with inconclusive effectiveness in earlier studies.
Overall, the theta wave measurement and behavior test gave similar results, said the senior author.
However, for a few of the experiments, such as a dose below the effective level of the first medication, the EEG test provided results that were more accurate -- more similar to the results found in patients than the behavior test -- said the senior author. Specifically, the EEG test showed a decrease in theta power measurement at the clinical dose but not the low dose, while the behavior test showed slower paw withdrawal at the low dose and the clinical dose. By indicating pain relief at a dose lower than the effective dose, the behavior test gave a false positive.
"The ability to detect false positive or false negative outcomes is crucial to the drug development process," the authors wrote. The senior author believes that the EEG test can aid researchers in identifying false positives in pre-clinical trials of new pain medications, improving the development process.
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