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利用结构或功能磁共振成像的多变量模式分析-在患有肌肉骨骼疼痛的个体和健康对照组中:系统评价。

Multivariate pattern analysis utilizing structural or functional MRI-In individuals with musculoskeletal pain and healthy controls: A systematic review.

机构信息

Recover Injury Research Centre, NHMRC CRE in Recovery Following Road Traffic Injury, Menzies Health Institute QLD, Griffith University, Gold Coast Campus, Southport, Queensland 4125, Australia.

Cognitive and Affective Neuroscience Laboratory, Department of Psychology and Neuroscience, Institute of Cognitive Science, The University of Colorado, Boulder, CO.

出版信息

Semin Arthritis Rheum. 2017 Dec;47(3):418-431. doi: 10.1016/j.semarthrit.2017.06.005. Epub 2017 Jun 15.

Abstract

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.

摘要

目的

本系统评价旨在系统地回顾与结构或功能磁共振成像(fMRI)后多元模式分析(MVPA)产生的结果相关的证据,以确定这种分析是否能够:a)区分骨骼肌肉疼痛患者和健康对照者,b)预测健康个体在受到有害刺激与非有害刺激时的疼痛感知。

方法

使用不同的关键词组合,通过 MVPA 对结构和功能 MRI 进行分析,分别对患有骨骼肌肉疼痛的个体和健康对照者进行了 MEDLINE、CINAHL、Embase、PEDro、Google Scholar、Cochrane 图书馆和 Web of Science 的系统筛选。纳入文章的参考文献列表也进行了手工搜索,以获取更多文献。对合格文章进行了偏倚风险评估,并由两名独立研究人员进行了审查。

结果

搜索查询返回了 18 篇符合纳入标准的文章。方法学质量从差到好不等。7 项研究调查了机器学习算法区分患者组和健康对照组的能力。总的来说,该综述表明 MVPA 可以区分骨骼肌肉疼痛患者和健康对照者,整体准确率从 53%到 94%不等。12 项研究利用健康对照组(将其作为自身对照组),在实验性疼痛范式中,旨在调查机器学习区分受有害刺激和非有害刺激个体的能力,“疼痛”检测率从 60%到 94%不等。然而,由于患者状况、研究方法和脑成像技术的显著异质性,导致了各种不同的发现,使得研究比较和正式结论具有挑战性。

结论

有初步和新兴的证据表明,结构或功能 MRI 的 MVPA 分析能够区分患者和健康对照者,也能够区分有害和无害刺激。本综述中未发现前瞻性研究,无法确定这些分析的预后或诊断能力或治疗反应性。未来的研究还将受益于将各种行为、基因型和表型数据结合到分析中,以协助开发敏感和特异的特征,从而指导未来的个体化患者治疗选择,并评估治疗如何发挥其作用。

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