Researchers are coming close to developing a tool to diagnose pain. This could mean never having to ask “does this hurt?” again.
One of the major hurdles in pain medicine is having to depend on self-reporting to measure the presence or absence of pain… which is highly subjective.
A team led by Stanford’s Sean Mackey used functional magnetic resonance imaging (fMRI) brain scans and a computer algorithm to accurately separate painful from non-painful heat sensations.
“What we have never done to date is show that you can use that pattern of information to determine whether someone is in pain or not,” Mackey says.
Coauthor Neil Chatterjee also of Stanford adds: “We thought, maybe we can’t make the perfect tool, but has anyone ever really tried doing this on a very, very basic level? It turned out to be surprisingly simple to do this.”
- Healthy subjects were put in the fMRI, which scientists use to study brain structures and patterns of activity.
- A heat probe was applied to their forearms to create moderate thermal pain. They reported a pain score of 7 out of 10 when the temperature rose to 115 degrees Fahrenheit.
- Brain patterns both with and without pain were recorded.
- A computer algorithm was used to create a model of what it thinks pain looks like.
It successfully identified pain 81% of the time. (Pictured, red for regions associated with painful stimuli, blue for non-painful.)
According to the Institute of Medicine, over 100 million Americans suffer from chronic pain, costing up to $600 billion a year in medical expenses and lost productivity. But what’s more interesting is that there’s a cultural bias against chronic pain sufferers as being weak or liars.
The team needs to figure out if these methods can measure various kinds of pain, such as chronic pain, and if they can accurately tease out other emotionally arousing states, such as anxiety or depression.
They hope to expand their pain detector to find brain signatures representing different levels and types of pain, a technology that might one day be useful in clinical practice, drug trials, and pain research.
"I'm hoping that we will see this technology as an objective biomarker of treatment responsiveness for clinical trials when testing a particular therapy or drug," Mackey says. "This will augment the patient's recorded pain, and give us a more objective measure of how we're actually impacting their pain and their pain system."
The study was published in the open access journal PLoS ONE this week.
Image from Brown et al.