Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study.

Background: The amygdala plays a crucial role in the central pathogenesis mechanism of primary dysmenorrhea (PDM). However, the detailed pain modulation principles of the amygdala in PDM remain unclear. Here, we applied the Granger causality analysis (GCA) to investigate the directional effective connectivity (EC) alterations in the amygdala network of PDM patients.

Methods: Thirty-seven patients with PDM and 38 healthy controls were enrolled in this study and underwent resting-state functional magnetic resonance imaging scans during the pain-free stage. GCA was employed to explore the amygdala-based EC network alteration in PDM. A multivariate pattern analysis (MVPA)-based machine learning approach was used to explore whether the altered amygdala EC could serve as an fMRI-based marker for classifying PDM and HC participants.

Results: Compared to the healthy control group, patients with PDM showed significantly decreased EC from the amygdala to the right superior frontal gyrus (SFG), right superior parietal lobe/middle occipital gyrus, and left middle cingulate cortex, whereas increased EC was found from the amygdala to the bilateral medial orbitofrontal cortex. In addition, increased EC was found from the bilateral SFG to the amygdala, and decreased EC was found from the medial orbitofrontal cortex, caudate nucleus to the amygdala. The increased EC from the right SFG to the amygdala was associated with a plasma prostaglandin E2 level in PDM. The MVPA based on an altered amygdala EC pattern yielded a total accuracy of 86.84% for classifying the patients with PDM and HC.

Conclusion: Our study is the first to combine MVPA and EC to explore brain function alteration in PDM. The results could advance understanding of the neural theory of PDM in specifying the pain-free period.