Matthew A. Howard1, Jonathan O'Muircheartaigh2, Kristina Krause1, Nathalie Massat3, Nadine Khawaja1, John P. Huggins4, William Vennart4, Tara F. Renton5, Andre Marquand1, Steven C R Williams1
1Neuroimaging, King's College London, Institute of Psychiatry, Camberwell, London, United Kingdom; 2Clinical Neuroscience, King's College London, Institute of Psychiatry, United Kingdom; 3Centre for Cancer Prevention, Queen Mary University Of London; 4Pfizer Global Research and Development; 5Dental Institute, King's College London, United Kingdom
Recent reports have described the application of arterial spin labelling (ASL), to interrogate perfusion changes associated with the central representation of ongoing pain. We used Gaussian Process Classification, a supervised 'machine learning' multivariate analysis technique, to provide probabilistic classification of 'No Pain' from 'Ongoing Pain' states, as experienced following wisdom tooth extraction, using only ASL-derived indices of regional cerebral blood flow (rCBF) in each state. GPC classified between states with accuracy above 90%; 80% accuracy could be maintained using only two rCBF maps per state. This methodology has potential to impact on efficient economic assessment of novel treatments for pain.