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

Purpose: The purpose of this study was to investigate the utility of data-driven analyses of functional magnetic resonance imaging (fMRT) data, by means of structural equation modeling, for the investigation of pain processing in fibromyalgia (FM). Patients and Methods: Datasets from two separate pain fMRT 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.

Automated summary

This study aimed to investigate the utility of data-driven analyses of fMRT data in examining pain processing in FM patients, compared to conventional model-driven approaches. The results showed that data-driven analyses identified significant group differences in pain processing that traditional model-driven analyses did not, suggesting that data-driven approaches can enhance the understanding of pain processing in both healthy controls and clinical populations.