Machine Learning Analysis Reveals Abnormal Static and Dynamic Low-Frequency Oscillations Indicative of Long-Term Menstrual Pain in Primary Dysmenorrhea Patients

Abstract

Shao-Gao Gui,1,2,* Ri-Bo Chen,2,* Yu-Lin Zhong,1 Xin Huang1 1Department of Ophthalmology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China; 2Department of Radiology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xin HuangDepartment of Ophthalmology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, No. 152, Ai Guo Road, Dong Hu District, Nanchang, 330006, Jiangxi, People’s Republic of ChinaTel [phone]Email [email protected]: Previous neuroimaging studies demonstrated that patients with primary dysmenorrhea (PD) exhibited dysfunctional resting-state brain activity. However, alterations of dynamic brain activity in PD patients have not been fully characterized.Purpose: Our study aimed to assess the effect of long-term menstrual pain on changes in static and dynamic neural activity in PD patients.Material and Methods: Twenty-eight PD patients and 28 healthy controls (HCs) underwent resting-state magnetic resonance imaging scans. The amplitude of low-frequency fluctuations (ALFF) and dynamic ALFF was used as classification features in a machine learning approach involving a support vector machine (SVM) classifier.Results: Compared with the HC group, PD patients showed significantly increased ALFF values in the right cerebellum_crus2, right rectus, left supplementary motor area, right superior frontal gyrus, right supplementary motor area, and left superior frontal medial gyrus. Additionally, PD patients showed significantly decreased ALFF values in the right middle temporal gyrus and left thalamus. PD patients also showed significantly increased dALFF values in the right fusiform, Vermis_10, right middle temporal gyrus, right putamen, right insula, left thalamus, right precentral gyrus, and right postcentral gyrus. Based on ALFF and dALFF values, the SVM classifier achieved respective overall accuracies of 96.36% and 85.45% and respective areas under the curve of 1.0 and 0.95.Conclusion: PD patients demonstrated abnormal static and dynamic brain activities that involved the default mode network, sensorimotor network, and pain-related subcortical nuclei. Moreover, ALFF and dALFF may offer sensitive biomarkers for distinguishing patients with PD from HCs.Keywords: primary dysmenorrhea, amplitude of low-frequency fluctuations, support vector machine

Keywords

• primary dysmenorrhea
• amplitude of low-frequency fluctuations
• support vector machine;