title: Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.
abstract: Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. We combined these acquisition and analysis methodologies in a well-characterized postsurgical pain model. The principal aims were (1) to assess the classification accuracy of rCBF indices acquired prior to and following surgical intervention and (2) to optimise the amount of data required to maintain accurate classification. Twenty male volunteers, requiring bilateral, lower jaw third molar extraction (TME), underwent ASL examination prior to and following individual left and right TME, representing presurgical and postsurgical states, respectively. Six ASL time points were acquired at each exam. Each ASL image was preceded by visual analogue scale assessments of alertness and subjective pain experiences. Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting.
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Centre for Neuroimaging Sciences, King's College London, London, United Kingdom.
authors.forename: Jonathan
authors.initials: J
authors.lastname: O'Muircheartaigh
authors.forename: Andre
authors.initials: A
authors.lastname: Marquand
authors.forename: Duncan J
authors.initials: DJ
authors.lastname: Hodkinson
authors.forename: Kristina
authors.initials: K
authors.lastname: Krause
authors.forename: Nadine
authors.initials: N
authors.lastname: Khawaja
authors.forename: Tara F
authors.initials: TF
authors.lastname: Renton
authors.forename: John P
authors.initials: JP
authors.lastname: Huggins
authors.forename: William
authors.initials: W
authors.lastname: Vennart
authors.forename: Steven C R
authors.initials: SC
authors.lastname: Williams
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Howard
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D002560:Cerebrovascular Circulation / Q000502:physiology*; D016002:Discriminant Analysis; D005240:Feasibility Studies; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008964:Molar, Third / Q000601:surgery; D015999:Multivariate Analysis; D016011:Normal Distribution; D010147:Pain Measurement; D010149:Postoperative Pain / Q000175:diagnosis / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D014081:Tooth Extraction / Q000009:adverse effects; D055815:Young Adult
keywords: arterial spin labeling; biomarker; machine learning; pain