How Brain Patterns Can Predict the Severity of Chronic Back Pain
Health experts have been searching for a long time for a reliable method to objectively measure pain.
A new study from Brigham and Women's Hospital promises to be an exciting discovery for anyone who suffers from chronic pain. With more than 100 million American sufferers, treatment of chronic pain is complex and presents many challenges.
This new study shows that specific brain patterns may help track severity of pain reported by patients. Not only that, it may be able to predict who is more likely to experience worsening of their chronic pain when performing maneuvers specifically designed to induce pain.
If further research shows that such brain patterns are a reliable indicator, this is a major step forward towards creating an objective scale for measuring pain.
Specifically, researchers from Brigham and Women's Hospital studied 16 adults with chronic back pain and 16 adults without pain. They used a brain imaging technique called arterial spin labeling to identify patterns in how different brain regions interacted with each other in these subjects.
Interestingly, they found that when a patient moved in a way that increased their back pain, a network of brain regions called Default Mode Network showed clear changes in the way they interacted. Typically, regions within the network became less connected with the rest of the network, while regions outside the network became more connected with it.
Similar observations were made in previous studies of fibromyalgia patients, suggesting that such changes in brain connectivity may reflect general features of chronic pain that are common to different patient populations.
This is the first study that uses arterial spin labeling to show that networking properties of the brain are affected by chronic pain. It is also the first study to use arterial spin labeling as a tool to evaluate how the brain encodes and reacts to chronic pain.
These study results suggest that resting default mode network connectivity may be a useful neuroimaging biomarker to assess chronic pain perception.