Pain is a subjective experience, and the only way to gauge that experience is for patients to self-report how much pain they feel. Unlike many other diseases and medical conditions, there is no “biomarker” of pain—no objective, measurable indicator of pain such as a blood test or brain scan. But, some researchers hope that patterns of brain activity, viewed with brain imaging technology, might one day serve this elusive role.
Now, a team of researchers has taken an early step toward that aim. Using brain imaging along with machine learning—a type of artificial intelligence where computers can learn—the group was able to detect brain activity patterns in individual patients with chronic low back pain (cLBP) that differed depending on how much pain they reported.
The investigators from Massachusetts General Hospital, Boston, US, presented the work in a poster at the 2016 annual meeting of the Society for Neuroscience (SfN). SfN is the world’s largest neuroscience conference for scientists and physicians seeking to understand the brain and nervous system. The most recent meeting took place November 12-16, 2016, in San Diego, US.
In a press conference held at the meeting with directors of the National Institutes of Health, Nora Volkow, Director of the National Institute on Drug Abuse (NIDA) and David Shurtleff, Deputy Director of the National Center for Complementary and Integrative Health (NCCIH), Bethesda, US, both bemoaned the lack of objective biomarkers as an impediment to advancing the understanding and treatment of chronic pain.
Shurtleff specifically touted the new work from senior author Bruce Rosen. “This research is a preliminary step in the right direction for developing a tool for objectively measuring a pain state. This could give us the insight we need for understanding brain mechanisms associated with various pain conditions,” Shurtleff later said in an email.
Machine learning reveals more than conventional approaches alone
Many previous imaging studies have used “evoked pain” in healthy people or chronic pain patients—that is, they inflict some hurt during imaging, by subjecting a person to painful heat, for instance. But the goal is to find brain activity patterns characteristic of ongoing pain, which is what patients complain of.
“It’s hard to turn clinical pain on and off,” said another senior author, Vitaly Napadow, referring to the pain that patients actually feel. “So [instead] we’ve used a strategy to exacerbate it.”
The researchers collected brain images from individual patients with cLBP when their pain was at a low, baseline state. Then the subjects performed physical maneuvers intended to temporarily increase their pain, including movements such as sit-ups or back-arching motions, and then underwent imaging once again.
Pain ratings increased in all 39 patients, by an average of 80 percent. By comparing brain images from individual patients in the two states, the researchers were able to learn more from their data than when comparing separate groups of patients.
To analyze the imaging data, the researchers first used a conventional method of statistical analysis. Predictably, in the high pain condition compared with low pain, brain activity was increased in regions previously associated with pain, including the thalamus, a sensory processing station in the brain. But when the researchers used machine learning, they identified other specific brain areas that were activated by exacerbated pain.
The primary somatosensory cortex (S1) is a brain area that contains a map of the body’s surface. When pain is evoked in a particular area of the body, activity in the part of the S1 cortex representing that area typically increases, whereas the rest of S1 will show less activity.
Interestingly, conventional analysis of the imaging data showed, as expected, that S1 activity in the cortex did decrease in areas representing non-back regions of the body, but no increase was detected in cortex representing the painful back regions. The machine learning approach, in contrast, revealed increased activation in the region of S1 that mapped to the painful back region.
Toward a biomarker
Napadow stressed that this work is in its early stages, and represents just one contribution among many that will be needed to find a biomarker of the pain that patients feel—but to what end? Would a biomarker be used for diagnostic purposes, to “prove” that someone does or does not suffer from chronic pain? Might it be used to deny legal, medical or financial benefits to patients? How would it be used as a tool to aid in treating patients?
These are open questions, and many researchers—Napadow among them—have concerns about how a potential biomarker might be misused.
One thing is certain, Napadow says: researchers are unlikely to find anything nearly as accurate for pain as a genetic test, where DNA evidence can confirm or deny a match between, for example, a sample of blood found at a crime scene and one from a suspect, with 99.99% accuracy.
“Even with that level of specificity, that sort of DNA testing still gets questioned in legal settings. And we are nowhere near that when it comes to an imaging biomarker for clinical pain,” according to Napadow.
While patient self-report of pain is a subjective measure, Napadow said, it should not be discounted. “Imaging is just part of the story,” he stressed.
Self-report can in fact be used in conjunction with imaging. For example, researchers might use imaging to probe responses to therapies, tracking patient-reported experiences along with imaging data. Another proposed use of brain imaging biomarkers of chronic pain is for teaching patients self-regulation of pain with imaging-based neurofeedback. –Stephani Sutherland
Stephani Sutherland, PhD, is a neuroscientist, yogi, and freelance writer in Southern California.