Whole-brain structural magnetic resonance imaging–based classification of primary dysmenorrhea in pain-free phase: a machine learning study

Abstract

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

“…PDM has been proposed to be part of the central sensitization syndromes together with several chronic pain conditions, including fibromyalgia, irritable bowel syndrome, idiopathic low back pain, headache and migraine ( Yunus, 2012 ; Iacovides et al, 2015 ). In recent years, using neuroimaging techniques, our group ( Shen et al, 2019 ; Zhang et al, 2019 ) and other groups ( Tu et al, 2009 ; Liu et al, 2018 ; Chen et al, 2019 ) have confirmed that PDM is associated with significant changes in the central nervous system’s anatomy, metabolism, and resting-state function. Although the exact mechanisms of the analgesic effects of acupuncture are not known, studies have postulated that acupuncture can alleviate pain by modulating brain regions and networks associated with pain processing ( Chen et al, 2015 ; Maeda et al, 2017 ).…”

Immediate Analgesic Effect of Acupuncture in Patients With Primary Dysmenorrhea: A fMRI Study

“…The PCU modulates chronic pain (Farmer et al, 2012) and acts on the autonomic nerve and descending inhibition systems to regulate the pain sensation caused by acute noxious stimulation (Kucyi et al, 2013). Abnormal changes in gray matter volume are observed in the PCU of patients with PDM (Tu et al, 2013;Liang et al, 2016;Chen et al, 2019). Moreover, patients with PDM have increased FC (Liu P. et al, 2017) and spontaneous brain activity (Jin et al, 2017) in the PCU, and acupuncture treatment significantly regulates this abnormal brain functional network connection (Cai et al, 2018).…”

Neuroimaging-based biomarkers for pain: state of the field and current directions

“…Recently, a study used whole-brain gray matter to discriminate between subjects with primary dysmenorrhea and healthy controls with an accuracy of 75.4% and 70.2% in a separate validation set. 22…”