使用结构方程建模技术的功能磁共振成像分析如何能增进我们对纤维肌痛中疼痛处理的理解。

How fMRI Analysis Using Structural Equation Modeling Techniques Can Improve Our Understanding of Pain Processing in Fibromyalgia.

作者信息

Warren Howard J M, Ioachim Gabriela, Powers Jocelyn M, Stroman Patrick W

机构信息

Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada.

Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.

出版信息

J Pain Res. 2021 Feb 10;14:381-398. doi: 10.2147/JPR.S290795. eCollection 2021.

DOI:10.2147/JPR.S290795

PMID:33603453

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7882802/

Abstract

PURPOSE

The purpose of this study was to investigate the utility of data-driven analyses of functional magnetic resonance imaging (fMRI) data, by means of structural equation modeling, for the investigation of pain processing in fibromyalgia (FM).

PATIENTS AND METHODS

Datasets from two separate pain fMRI studies involving healthy controls (HC) and participants with FM were re-analyzed using both a conventional model-driven approach and a data-driven approach, and the results from these analyses were compared. The first dataset contained 15 women with FM and 15 women as healthy controls. The second dataset contained 15 women with FM and 11 women as healthy controls.

RESULTS

Consistent with previous studies, the model-driven analyses did not identify differences in pain processing between the HC and FM study groups in both datasets. On the other hand, the data-driven analyses identified significant group differences in both datasets.

CONCLUSION

Data-driven analyses can enhance our understanding of pain processing in healthy controls and in clinical populations by identifying activity associated with pain processing specific to the clinical groups that conventional model-driven analyses may miss.

摘要