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Machine Learning Analysis Reveals Abnormal Static and Dynamic Low-Frequency Oscillations Indicative of Long-Term Menstrual Pain in Primary Dysmenorrhea Patients
- Source :
- Journal of Pain Research
- Publication Year :
- 2021
- Publisher :
- Informa UK Limited, 2021.
-
Abstract
- Shao-Gao Gui,1,2,* Ri-Bo Chen,2,* Yu-Lin Zhong,1 Xin Huang1 1Department of Ophthalmology, Jiangxi Provincial Peopleâs Hospital Affiliated to Nanchang University, Nanchang, 330006, Jiangxi, Peopleâs Republic of China; 2Department of Radiology, Jiangxi Provincial Peopleâs Hospital Affiliated to Nanchang University, Nanchang, 330006, Jiangxi, Peopleâs Republic of China*These authors contributed equally to this workCorrespondence: Xin HuangDepartment of Ophthalmology, Jiangxi Provincial Peopleâs Hospital Affiliated to Nanchang University, No. 152, Ai Guo Road, Dong Hu District, Nanchang, 330006, Jiangxi, Peopleâs Republic of ChinaTel +86 15879215294Email 334966891@qq.comBackground: 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
- Subjects :
- Cerebellum
medicine.diagnostic_test
Brain activity and meditation
business.industry
Amplitude of low frequency fluctuations
Magnetic resonance imaging
Machine learning
computer.software_genre
Anesthesiology and Pain Medicine
medicine.anatomical_structure
Neuroimaging
Gyrus
medicine
amplitude of low-frequency fluctuations
support vector machine
Menstrual pain
Artificial intelligence
Journal of Pain Research
business
computer
primary dysmenorrhea
Default mode network
Original Research
Subjects
Details
- ISSN :
- 11787090
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- Journal of Pain Research
- Accession number :
- edsair.doi.dedup.....4fcdff63c931753f4363ee90469e1160