Over 3 million Australians live with chronic pain, with current treatments, particularly for pain following nerve injury, i.e. neuropathic pain, largely ineffective. The major roadblock for effective treatment development is our limited understanding of the underlying pathophysiology as well as individual variability. The assumption that when two individuals are classified into the same neuropathic pain category, they have the same underlying neurobiology is not valid. Instead, stratifying individuals based on biological measures would provide the opportunity to develop treatments targeting individual underlying mechanisms. We propose a series of magnetic resonance imaging combined with quantitative sensory testing, genetic, immune, and psychosocial measures that will provide a unique set of integrated data from the cellular level to the whole brain and from peripheral to central processes. Using these measures, we will define the individual biological characteristics of each chronic pain patient and use machine learning classification models to stratify individuals into groups with similar underlying mechanisms. An understanding of individual variations in biological processes and a biologically-based classification system is needed. Such a system would significantly improve our capacity for diagnostic stratification, treatment guidance, and novel treatment development to improve overall care.
Dr Alister Ramachandran, Westmead Pain Management Centre, NSW; Professor Luke Henderson, Dr Lewis Crawford, University of Sydney, NSW; Professor Chris Peck, Dr Hongfei Yang, The National University of Singapore.
The project was awarded A$70,000 funding through the ANZCA research grants program for 2025.