More than 100 million individuals in the U.S. suffer from chronic pain. This is more than the combined total affected by heart disease, cancer and diabetes. About half of these individuals are partially or totally disabled by chronic pain and the associated annual costs, which exceed $635 billion in the U.S. alone.
Despite the immense size of the problem, there are currently no reliable biomarkers that can predict if someone will transition from acute to chronic pain or which treatment path will be most promising.
Recent neuroscientific studies show that the transition from acute to chronic pain is accompanied by a shift from peripheral (body-centered) to central (brain-centered) processing. This indicates that chronic pain might be learned or expected, and that it may be generated by the brain in a top-down fashion — even in the absence of nociceptive input from the body.
Researchers are using computational models in combination with measurements of perception and learning to quantify the extent to which central processes contribute to an individual's experience of pain. These "computational biomarkers" are then used to predict person-specific pain trajectories (prevention) and suggest ideal courses of treatment (treatment allocation).
To implement and validate their approach, the researchers use SOMA — a mobile app which combines symptom tracking with neuroscientific assessments and tailored interventions. SOMA is designed to directly support individuals on their journeys to overcome pain.
This project tests if novel individual biomarkers of interoceptive learning and perception could predict the transition to chronic pain. These computational biomarkers could trigger early interventions to prevent pain "chronification" and protect millions from the disabling burden of chronic pain.