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. Hum Brain Mapp 36:633–642, 2015. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
Highlights
Despite decades of effort and investment, effective pain management remains an unmet need worldwide [Kupers and Kehlet, 2006]
Supervised “machine learning” (ML) pattern classifiers potentially offer the desirable quality of predicting class membership of new individuals, for example, whether a new patient is in pain or might respond to treatment [Marquand et al, 2012]
We have demonstrated accurate discrimination of ongoing postsurgical pain from pain-free states in the same individuals
Summary
Despite decades of effort and investment, effective pain management remains an unmet need worldwide [Kupers and Kehlet, 2006]. Resting-state fMRI offers promise but the mechanisms underlying correlations between brain regions during the pain experience remain poorly understood [Napadow et al, 2010] Another MRI-based technique, arterial spin labeling (ASL), provides noninvasive, quantitative indices of cerebral blood flow (CBF) with the sensitivity to detect “tonic” states over the course of minutes [Aguirre et al, 2002]; ideally suitable for examining persistent or on-going pain. Supervised “machine learning” (ML) pattern classifiers potentially offer the desirable quality of predicting class membership of new individuals, for example, whether a new patient is in pain or might respond to treatment [Marquand et al, 2012] Despite their impact in the field to date, ML techniques have yet to have been applied to the critical challenges of on-going clinical pain. The two principal aims of this study were: (i) to determine GPC accuracy in discriminating presurgical from postsurgical states, following left and right TME; (ii) to understand the temporal effects of acquiring multiple ASL scans on this classification accuracy
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