Announced on Aug 3, 2018
It’s easy to describe how much pain you’re in when you stub your toe. However, describing chronic pain—persistent pain that lasts longer than three months—is much more complex. Not only does the intensity of chronic pain fluctuate from day to day, but the features of pain may also change over time.
To better understand what happens in the brain during chronic pain, clinicians and researchers are using functional magnetic resonance imaging (fMRI), a sophisticated technology that enables them to visualize the communication within or among brain networks. fMRI can be used to measure communication as a single snapshot or as a flexible dynamic interaction over a longer period of time.
However, it is not well understood how these measurements relate to chronic pain or whether they better reflect a person’s current state of pain or the overall intensity of their chronic pain over time.
To address this gap in knowledge, Krembil Senior Scientist Dr. Karen Davis and her PhD student Joshua Cheng initiated a study in research participants with or without ankylosing spondylitis, a form of arthritis that causes chronic back pain.
Dr. Davis’s research team collected fMRI measurements of brain network communication that reflect a static snapshot and a dynamic interaction over time, and also asked participants to rate their current pain and their average monthly pain using a questionnaire. Computer-based machine learning was then used to create two chronic pain brain models based on the fMRI measurements and pain scores—one to represent current ‘state’ pain and another to represent average ‘trait’ pain over a period of time.
The team found that three specific brain networks displayed abnormal communication in people with ankylosing spondylitis, all of whom experience chronic pain. They also found that different patterns of communication between these networks were related to current or monthly trait pain ratings. In general, the dynamic fMRI measurements taken over time were more informative than the static snapshots in relating communication to chronic pain.
“Our study is the first to reveal how changes in the communication patterns of brain networks relate to fundamental features and timing of chronic pain,” explains Dr. Davis. “These findings shed new light on the complexities of chronic pain and will contribute to the development of new solutions for those with this long-term and disabling condition.”
This work was supported by the Canadian Institutes of Health Research, the Canadian Chronic Pain Network, The Mayday Fund and the Toronto General & Western Hospital Foundation.
Cheng JC, Rogachov A, Hemington KS, Kucyi A, Bosma RL, Lindquist MA, Inman RD, Davis KD. Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain. Pain. 2018 Apr 26. doi: 10.1097/j.pain.0000000000001264.
Dr. Karen Davis, Senior Scientist, Krembil Research Institute