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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| Vydáno v: | Human brain mapping Ročník 36; číslo 2; s. 633 - 642 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Blackwell Publishing Ltd
01.02.2015
John Wiley & Sons, Inc John Wiley and Sons Inc |
| Témata: | |
| ISSN: | 1065-9471, 1097-0193, 1097-0193 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| Bibliografie: | Wellcome Trust and EPSRC - No. WT 088641/Z/09/Z ArticleID:HBM22652 UK Medical Research Council istex:1A3607440591C99CD9F9421E4ED43D5F3BF6C717 Medical Research Council Developmental Pathway Funding Scheme (MR/J005142/1) Sir Henry Wellcome Postdoctoral Fellowship awarded by the Wellcome Trust (WT 096195 to J.O'M.) Pfizer Global Research and Development UK ark:/67375/WNG-HP2ZDKZ5-N O'Muircheartaigh and Howard contributed equally to this work. Conflict of interest: The collection of the data was funded by Pfizer Global Research and Development UK. MAH and KK were paid on grant income from this source. JPH and WV were employees of Pfizer. DJH and MAH were paid with grant income from the MRC. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 1065-9471 1097-0193 1097-0193 |
| DOI: | 10.1002/hbm.22652 |