Abstracts from the International Pelvic Pain Society (IPPS) annual scientific meeting on pelvic pain 2023

Abstract

Introduction: Data demonstrating abnormalities in brain structure and functional connectivity have supported the notion that menstrual pain may be related to deficits in central pain processing. We aimed to investigate the role of the triple network model of brain networks implicated in psychiatric disorders in the encoding of the menstrual pain, pain interference, and lifetime burden of menstrual pain in adolescent girls. Methods: One hundred adolescent girls (ages 13–19) completed a 6-minute resting state fMRI and rated menstrual pain and menstrual pain interference. Lifetime burden of menstrual pain reflected the total number of painful menstrual periods. Thirty resting-state networks were estimated using an unsupervised machine learning method for group independent component analysis. Networks of interest included cingulo-opercular salience (SN), central executive (CEN), and default mode (DMN) networks. Dual regression was used to extract subject-specific network maps corresponding to each a priori network. FSL Randomise was used for the estimation of general linear models and inference to test associations between network connectivity and menstrual pain measures ( P , 0.05 corrected). Results: Greater connectivity of SN with amygdala, CEN with lateral orbitofrontal cortex, and CEN with anterior insula was associated with higher menstrual pain. Higher pain interference was associated with greater connectivity between SN and widespread brain areas that share overlap with DMN and CEN. By contrast, higher lifetime burden was associated with reduced connectivity within DMN. Disclosure: Any of the authors act as a consultant, employee, or shareholder of an industry for: Bayer Healthcare, Mahana Therapeutics.