Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain

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 h...

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Published in:Human brain mapping 2015-02, Vol.36 (2), p.633-642
Main Authors: O'Muircheartaigh, Jonathan, Marquand, Andre, Hodkinson, Duncan J., Krause, Kristina, Khawaja, Nadine, Renton, Tara F., Huggins, John P., Vennart, William, Williams, Steven C.R., Howard, Matthew A.
Format: Article
Language:English
Subjects:
Adult
arterial spin labeling
Artificial Intelligence
biomarker
Cerebrovascular Circulation - physiology
Discriminant Analysis
Feasibility Studies
Humans
machine learning
Magnetic Resonance Imaging - methods
Male
Molar, Third - surgery
Multivariate Analysis
Normal Distribution
pain
Pain Measurement
Pain, Postoperative - diagnosis
Pain, Postoperative - physiopathology
Sensitivity and Specificity
Tooth Extraction - adverse effects
Young Adult
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Summary: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.
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.22652