Multivariate pattern analysis utilizing structural or functional MRI—In individuals with musculoskeletal pain and healthy controls: A systematic review
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Author(s)
Smith, Ashley
Lopez-Sola, Marina
McMahon, Katie
Pedler, Ashley
Sterling, Michele
Year published
2017
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Show full item recordAbstract
Objective: The purpose of this systematic review is to systematically review the evidence relating to
findings generated by multivariate pattern analysis (MVPA) following structural or functional magnetic
resonance imaging (fMRI) to determine if this analysis is able to: a) Discriminate between individuals
with musculoskeletal pain and healthy controls, b) Predict pain perception in healthy individuals
stimulated with a noxious stimulus compared to those stimulated with a non-noxious stimulus.
Methods: MEDLINE, CINAHL, Embase, PEDro, Google Scholar, Cochrane library and Web of Science were
systematically screened for relevant ...
View more >Objective: The purpose of this systematic review is to systematically review the evidence relating to findings generated by multivariate pattern analysis (MVPA) following structural or functional magnetic resonance imaging (fMRI) to determine if this analysis is able to: a) Discriminate between individuals with musculoskeletal pain and healthy controls, b) Predict pain perception in healthy individuals stimulated with a noxious stimulus compared to those stimulated with a non-noxious stimulus. Methods: MEDLINE, CINAHL, Embase, PEDro, Google Scholar, Cochrane library and Web of Science were systematically screened for relevant literature using different combinations of keywords regarding structural and functional MRI analysed with MVPA, both in individuals with musculoskeletal pain and healthy controls. Reference lists of included articles were hand-searched for additional literature. Eligible articles were assessed on risk of bias and reviewed by two independent researchers. Results: The search query returned 18 articles meeting the inclusion criteria. Methodological quality varied from poor to good. Seven studies investigated the ability of machine-learning algorithms to differentiate patient groups from healthy control participants. Overall, the review demonstrated that MVPA can discriminate between individuals with MSK pain and healthy controls with an overall accuracy ranging from 53% to 94%. Twelve studies utilized healthy control participants (using them as their own controls), during experimental pain paradigms aimed to investigate the ability of machine-learning to differentiate individuals stimulated with noxious stimuli from those stimulated with non-noxious stimuli, with ‘pain’ detection rates ranging from 60% to 94%. However, significant heterogeneity in patient conditions, study methodology and brain imaging techniques resulted in various findings that make study comparisons and formal conclusions challenging. Conclusion: There is preliminary and emerging evidence that MVPA analyses of structural or functional MRI are able to discriminate between patients and healthy controls, and also discriminate between noxious and non-noxious stimulation. No prospective studies were found in this review to allow determination of the prognostic or diagnostic capabilities or treatment responsiveness of these analyses. Future studies would also benefit from combining various behavioural, genotype and phenotype data into analyses to assist with development of sensitive and specific signatures that could guide future individualized patient treatment options and evaluate how treatments exert their effects.
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View more >Objective: The purpose of this systematic review is to systematically review the evidence relating to findings generated by multivariate pattern analysis (MVPA) following structural or functional magnetic resonance imaging (fMRI) to determine if this analysis is able to: a) Discriminate between individuals with musculoskeletal pain and healthy controls, b) Predict pain perception in healthy individuals stimulated with a noxious stimulus compared to those stimulated with a non-noxious stimulus. Methods: MEDLINE, CINAHL, Embase, PEDro, Google Scholar, Cochrane library and Web of Science were systematically screened for relevant literature using different combinations of keywords regarding structural and functional MRI analysed with MVPA, both in individuals with musculoskeletal pain and healthy controls. Reference lists of included articles were hand-searched for additional literature. Eligible articles were assessed on risk of bias and reviewed by two independent researchers. Results: The search query returned 18 articles meeting the inclusion criteria. Methodological quality varied from poor to good. Seven studies investigated the ability of machine-learning algorithms to differentiate patient groups from healthy control participants. Overall, the review demonstrated that MVPA can discriminate between individuals with MSK pain and healthy controls with an overall accuracy ranging from 53% to 94%. Twelve studies utilized healthy control participants (using them as their own controls), during experimental pain paradigms aimed to investigate the ability of machine-learning to differentiate individuals stimulated with noxious stimuli from those stimulated with non-noxious stimuli, with ‘pain’ detection rates ranging from 60% to 94%. However, significant heterogeneity in patient conditions, study methodology and brain imaging techniques resulted in various findings that make study comparisons and formal conclusions challenging. Conclusion: There is preliminary and emerging evidence that MVPA analyses of structural or functional MRI are able to discriminate between patients and healthy controls, and also discriminate between noxious and non-noxious stimulation. No prospective studies were found in this review to allow determination of the prognostic or diagnostic capabilities or treatment responsiveness of these analyses. Future studies would also benefit from combining various behavioural, genotype and phenotype data into analyses to assist with development of sensitive and specific signatures that could guide future individualized patient treatment options and evaluate how treatments exert their effects.
View less >
Journal Title
Seminars in Arthritis and Rheumatism
Copyright Statement
© 2017 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Note
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Subject
Clinical sciences
Health services and systems
Public health