Machine learning finds brain activity differs in low and high pain states during chronic low back pain
In an early step toward finding a brain imaging biomarker for pain, researchers have detected brain activity patterns in individual patients with chronic low back pain (cLBP) that differ depending on the pain level that the patients experience.
The team from Massachusetts General Hospital, Boston, US, used machine learning algorithms to analyze brain imaging data captured with arterial spin labeling (ASL).
Pain is a subjective experience, and the only way to gauge that experience is through patient self-report. Unlike many other diseases and medical conditions, there is no validated biomarker of pain—no blood test or brain scan. Some researchers hope that patterns of brain activity might one day serve this elusive role.
A machine learning approach
In many previous imaging studies, researchers have used strategies to evoke pain in healthy people or chronic pain patients, but the goal is to find the brain activity signatures of sustained clinical pain.
A significant problem with many pain research studies is that induced pain is NOT chronic pain.
“It’s hard to turn clinical pain on and off,” said another senior author, Vitaly Napadow. “So we’ve used a strategy to exacerbate it.”
So the researchers looked at brain images from individual patients with cLBP when their pain was at a low, baseline state and compared them to images after each patient performed physical maneuvers intended to temporarily increase pain.
The maneuvers included movements such as sit-ups or back-arching motions. Pain ratings increased in all 39 patients, by an average of 80 percent.
Yet such exercise is exactly what is recommended now as a treatment for back pain!
The approach “is really powerful, because it allows us to do paired analysis in the same patients when they are in high and low pain states,” Napadow said.
This is a significant improvement.
when Lee used machine learning algorithms, which allowed him to compare low and high pain states in individual patients, these algorithms identified other specific brain areas that were activated by exacerbated pain
The primary somatosensory cortex (S1) is a brain area that contains a homunculus—that is, a somatotopic map of the body’s surface. When pain is evoked in a particular area of the body, activity in the 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 somatosensory cortical activity did decrease in areas representing non-back regions, but no increase was detected in cortex representing the painful back regions.
The SVM machine learning algorithm, in contrast, revealed increased activation in S1 that mapped to the painful back region.
Toward a biomarker
Napadow stressed that this work is in its early stages, and that it represents just one contribution among many that will be needed to find a validated biomarker of clinical pain—but to what end?
many researchers—Napadow among them—have concerns about how a potential biomarker might be misused.
Pain patients are worried that our pain could be dismissed if it is a different kind that appears on such images or that our brains do not respond as a “typical brain”.
researchers might use imaging to probe responses to therapies, tracking patient-reported experiences along with imaging data.
Another proposed use of signatures of chronic pain is for teaching patients self-regulation of pain.
For example, several groups are now using real-time fMRI-guided neurofeedback to teach chronic pain patients to control their brain activity (for review, see Chapin et al., 2012).
Author: Stephani Sutherland, PhD, is a neuroscientist, yogi, and freelance writer in Southern California.