Pain management is one of the most difficult areas of medicine. Clinicians have little to go on beyond patients' self-reports, which can be highly variable. If doctors underestimate a patient's pain, they risk causing unnecessary suffering, but if they employ a better-safe-than-sorry approach, they risk over-medicating with drugs that are increasingly associated with abuse.
There have been various attempts to quantify pain as a physiological phenomenon, but none have proved effective enough to replace self-reporting. Now researchers at Stanford University have published a study of a promising fMRI-based solution.
The researchers used a machine-learning algorithm to recognize specific patterns in the fMRI data. They then found an association between certain patterns and pain in both self-reporting and non-reporting patients, implying a high degree of similarity across subjects. Although specific brain areas, such as the secondary somatosensory cortex, were implicated in the study, the researchers found that a whole-brain approach was more accurate at predicting pain than any individual brain region on its own (86.6 percent, compared to 71.9 percent for the secondary somatosensory cortex and 64.3 percent for a region called the mid-insular cortex).
The next step is to try to make the model robust enough to distinguish between different types of pain--for example, thermal versus mechanical--and to localize where on the body the pain is being experienced. Physicians will probably always have to rely on patient descriptions of pain to some degree, but experiments such as these offer hope that they will soon be able to bolster those qualitative ratings with more objective measurements, which should improve treatment outcomes.
Photo, the National Library of Medicine