There is no funding attached to this project. Students must obtain their own funding. The funding level is set by RCUK (Research Council, UK) View Website as “Doctoral Research in Medicine” (Band 3, higher level) which includes the cost of the nurse and scans.
Han Y, Smith MT. Pathobiology of cancer chemotherapy-induced peripheral neuropathy (CIPN). Frontiers in pharmacology. 2013;4:156.
Mouraux A, Iannetti GD. The search for pain biomarkers in the human brain. Brain. 2018;141(12):3290-307.
Schweinhardt P, Glynn C, Brooks J, McQuay H, Jack T, Chessell I, et al. An fMRI study of cerebral processing of brush-evoked allodynia in neuropathic pain patients. Neuroimage. 2006;32(1):256-65.
Scott AC, McConnell S, Laird B, Colvin L, Fallon M. Quantitative Sensory Testing to assess the sensory characteristics of cancer-induced bone pain after radiotherapy and potential clinical biomarkers of response. European journal of pain. 2012;16(1):123-33.
Dundee machine learning references:
Mwangi, B., Ebmeier K.P., Matthews, K.M., Steele, J.D. (2012) "Multicentre Diagnostic Classification of Individual Structural Neuroimaging Scans from Patients with Major Depressive Disorder", Brain 135(1) 1508-21
Johnston, B.A.., Tolomeo, S, Gradin, V., Christmas, D., Matthews, K., Steele J.D. (2015) “Failure of Hippocampal Deactivation during Loss Events in Treatment-Resistant Depression” Brain138(9) 2766-76
Emser, T.S., Johnston, B.A., Steele, J.D., Kooij, S., Thorell, L., Christiansen, H. (2018) “Assessing ADHD Symptoms in Children and Adolescents: Evaluating the Role of Objective Measures using the OBTEST”, Behavioural and Brain Functions (in press)
Milders M, Johnston B, Steele J.D. (2016) “Using Machine Learning to Predict Return to Work after Traumatic Brain Injury” (2016) Brain Injury, 30(5-6), 587-588
Johnston, B.A., Coghill, D., Matthews, K., Steele, J.D. (2015) "Predicting Methylphenidate Response in Attention Deficit Hyperactivity Disorder: a Preliminary Study" Journal of Psychopharmacology 29(1), 24-30
Johnston, B.A., Steele, J.D., Tolomeo, S., Christmas, D., Matthews, K.M. (2015) ‘Structural MRI-based predictions with Treatment-Refractory Depression’ Plos One (in press)
Johnston, B.A., Mwangi, B., Matthews, K., Coghill, D., Konrad, K., Steele, J.D. (2014) “Brainstem Abnormalities in ADHD Support High Accuracy Individual Diagnostic Classification” Human Brain Mapping 35(10:5179-89
Other machine learning references specifically linked to pain research:
Lotsch, J., Sipila, R., Tasmuth, T., Kringel, D., Estlander, A. M., Meretoja, T., Kalso, E. and Ultsch, A. (2018) “Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy” 171(2) 399-411
Lotsch, J., Sipila, R., Dimova, V. and Kalso, E. (2018) “Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery” 121(5) , 1123-1132
Lotsch, J., Ultsch, A. and Kalso, E. (2017) “Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis”, 119(4), 821-829