Prediction of pain intensity with uterine morphological features and brain microstructural and functional properties in women with primary dysmenorrhea

Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the...

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Veröffentlicht in:	Brain imaging and behavior Jg. 15; H. 3; S. 1580 - 1588
Hauptverfasser:	Quan, Shilan, Yang, Jing, Dun, Wanghuan, Wang, Ke, Liu, Hongjuan, Liu, Jixin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2021 Springer Nature B.V
Schlagworte:	Biomedical and Life Sciences Biomedicine Brain Cramps Diffusion Feature extraction Iterative methods Magnetic resonance imaging Menstruation Morphology Myometrium Neural networks Neuropsychology Neuroradiology Neurosciences Optimization Original Research Pain Performance prediction Permutations Predictions Psychiatry Regression analysis Regression models Structure-function relationships Substantia alba Substantia grisea Texture Thalamus Uterus Primary dysmenorrhea Uterine morphology Left thalamus Prediction Machine learning
ISSN:	1931-7557, 1931-7565, 1931-7565
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Abstract	Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the brain extracted from periovulatory phases for the intensity of menstrual pain as rated by women with PDM during their subsequent menstrual period. Forty-five women with PDM were recruited and classified into the high and mild pain intensity groups. Pelvic MRI was employed to extract the uterine texture features. White matter diffusion properties, grey matter and functional connectivity features were extracted as brain features. Multivariate logistic regression models with iteration optimization were built for classifying different pain intensity groups. Texture features from myometrium and uterine junction zone had outstanding prediction performance with an area under the receiver operating characteristic (AUC) of 0.96 ( P < 0.05, permutation test), and diffusion properties along the thalamic fiber bundles were the most discriminative features with AUC of 0.95. Applying features from uterus and brain together, we could gain better prediction performance. Our results indicated that accumulated differences in menstrual pain were associated not only with uterine structure but also diffusion properties of thalamic-related fiber tracts, suggesting that treatment options of PDM patients may be expanded from only being able to manage pain in the uterus focusing on the functional/structural modifications of the pain processing system.
AbstractList	Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the brain extracted from periovulatory phases for the intensity of menstrual pain as rated by women with PDM during their subsequent menstrual period. Forty-five women with PDM were recruited and classified into the high and mild pain intensity groups. Pelvic MRI was employed to extract the uterine texture features. White matter diffusion properties, grey matter and functional connectivity features were extracted as brain features. Multivariate logistic regression models with iteration optimization were built for classifying different pain intensity groups. Texture features from myometrium and uterine junction zone had outstanding prediction performance with an area under the receiver operating characteristic (AUC) of 0.96 (P < 0.05, permutation test), and diffusion properties along the thalamic fiber bundles were the most discriminative features with AUC of 0.95. Applying features from uterus and brain together, we could gain better prediction performance. Our results indicated that accumulated differences in menstrual pain were associated not only with uterine structure but also diffusion properties of thalamic-related fiber tracts, suggesting that treatment options of PDM patients may be expanded from only being able to manage pain in the uterus focusing on the functional/structural modifications of the pain processing system. Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the brain extracted from periovulatory phases for the intensity of menstrual pain as rated by women with PDM during their subsequent menstrual period. Forty-five women with PDM were recruited and classified into the high and mild pain intensity groups. Pelvic MRI was employed to extract the uterine texture features. White matter diffusion properties, grey matter and functional connectivity features were extracted as brain features. Multivariate logistic regression models with iteration optimization were built for classifying different pain intensity groups. Texture features from myometrium and uterine junction zone had outstanding prediction performance with an area under the receiver operating characteristic (AUC) of 0.96 (P < 0.05, permutation test), and diffusion properties along the thalamic fiber bundles were the most discriminative features with AUC of 0.95. Applying features from uterus and brain together, we could gain better prediction performance. Our results indicated that accumulated differences in menstrual pain were associated not only with uterine structure but also diffusion properties of thalamic-related fiber tracts, suggesting that treatment options of PDM patients may be expanded from only being able to manage pain in the uterus focusing on the functional/structural modifications of the pain processing system.Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the brain extracted from periovulatory phases for the intensity of menstrual pain as rated by women with PDM during their subsequent menstrual period. Forty-five women with PDM were recruited and classified into the high and mild pain intensity groups. Pelvic MRI was employed to extract the uterine texture features. White matter diffusion properties, grey matter and functional connectivity features were extracted as brain features. Multivariate logistic regression models with iteration optimization were built for classifying different pain intensity groups. Texture features from myometrium and uterine junction zone had outstanding prediction performance with an area under the receiver operating characteristic (AUC) of 0.96 (P < 0.05, permutation test), and diffusion properties along the thalamic fiber bundles were the most discriminative features with AUC of 0.95. Applying features from uterus and brain together, we could gain better prediction performance. Our results indicated that accumulated differences in menstrual pain were associated not only with uterine structure but also diffusion properties of thalamic-related fiber tracts, suggesting that treatment options of PDM patients may be expanded from only being able to manage pain in the uterus focusing on the functional/structural modifications of the pain processing system. Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the brain extracted from periovulatory phases for the intensity of menstrual pain as rated by women with PDM during their subsequent menstrual period. Forty-five women with PDM were recruited and classified into the high and mild pain intensity groups. Pelvic MRI was employed to extract the uterine texture features. White matter diffusion properties, grey matter and functional connectivity features were extracted as brain features. Multivariate logistic regression models with iteration optimization were built for classifying different pain intensity groups. Texture features from myometrium and uterine junction zone had outstanding prediction performance with an area under the receiver operating characteristic (AUC) of 0.96 ( P < 0.05, permutation test), and diffusion properties along the thalamic fiber bundles were the most discriminative features with AUC of 0.95. Applying features from uterus and brain together, we could gain better prediction performance. Our results indicated that accumulated differences in menstrual pain were associated not only with uterine structure but also diffusion properties of thalamic-related fiber tracts, suggesting that treatment options of PDM patients may be expanded from only being able to manage pain in the uterus focusing on the functional/structural modifications of the pain processing system.
Author	Liu, Jixin Yang, Jing Dun, Wanghuan Quan, Shilan Liu, Hongjuan Wang, Ke
Author_xml	– sequence: 1 givenname: Shilan surname: Quan fullname: Quan, Shilan organization: Center for Brain Imaging, School of Life Science and Technology, Xidian University, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education – sequence: 2 givenname: Jing surname: Yang fullname: Yang, Jing organization: Department of Medical Imaging, First Affiliated Hospital of Xi’an Jiaotong University – sequence: 3 givenname: Wanghuan surname: Dun fullname: Dun, Wanghuan organization: Department of Medical Imaging, First Affiliated Hospital of Xi’an Jiaotong University – sequence: 4 givenname: Ke surname: Wang fullname: Wang, Ke organization: Department of Medical Imaging, First Affiliated Hospital of Xi’an Jiaotong University – sequence: 5 givenname: Hongjuan surname: Liu fullname: Liu, Hongjuan email: liuhj423@163.com organization: Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University – sequence: 6 givenname: Jixin orcidid: 0000-0002-4239-1201 surname: Liu fullname: Liu, Jixin email: liujixin@xidian.edu.cn organization: Center for Brain Imaging, School of Life Science and Technology, Xidian University, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education
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Keywords	Primary dysmenorrhea Uterine morphology Left thalamus Prediction Machine learning
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SubjectTerms	Biomedical and Life Sciences Biomedicine Brain Cramps Diffusion Feature extraction Iterative methods Magnetic resonance imaging Menstruation Morphology Myometrium Neural networks Neuropsychology Neuroradiology Neurosciences Optimization Original Research Pain Performance prediction Permutations Predictions Psychiatry Regression analysis Regression models Structure-function relationships Substantia alba Substantia grisea Texture Thalamus Uterus
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Title	Prediction of pain intensity with uterine morphological features and brain microstructural and functional properties in women with primary dysmenorrhea
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