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

Authors Warren HJM, Ioachim G, Powers JM, Stroman PW

Received 10 November 2020

Accepted for publication 16 January 2021

Published 10 February 2021 Volume 2021:14 Pages 381—398


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Dr Michael Schatman

Howard JM Warren,1 Gabriela Ioachim,1 Jocelyn M Powers,1 Patrick W Stroman1– 3

1Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada; 2Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada; 3Department of Physics, Queen’s University, Kingston, Ontario, Canada

Correspondence: Patrick W Stroman
Queen’s University, Centre for Neuroscience Studies, 2nd Floor, Botterell Hall, 18 Stuart Street, Kingston, K7L 3N6, Ontario, Canada
Tel +1-613-533-3245

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.

Keywords: human, brain, pain, neuroimaging, fibromyalgia, chronic pain

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