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Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study

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Abstract

Background

The amygdala plays a crucial role in the central pathogenesis mechanism of primary dysmenorrhea (PDM). However, the detailed pain modulation principles of the amygdala in PDM remain unclear. Here, we applied the Granger causality analysis (GCA) to investigate the directional effective connectivity (EC) alterations in the amygdala network of PDM patients.

Methods

Thirty-seven patients with PDM and 38 healthy controls were enrolled in this study and underwent resting-state functional magnetic resonance imaging scans during the pain-free stage. GCA was employed to explore the amygdala-based EC network alteration in PDM. A multivariate pattern analysis (MVPA)-based machine learning approach was used to explore whether the altered amygdala EC could serve as an fMRI-based marker for classifying PDM and HC participants.

Results

Compared to the healthy control group, patients with PDM showed significantly decreased EC from the amygdala to the right superior frontal gyrus (SFG), right superior parietal lobe/middle occipital gyrus, and left middle cingulate cortex, whereas increased EC was found from the amygdala to the bilateral medial orbitofrontal cortex. In addition, increased EC was found from the bilateral SFG to the amygdala, and decreased EC was found from the medial orbitofrontal cortex, caudate nucleus to the amygdala. The increased EC from the right SFG to the amygdala was associated with a plasma prostaglandin E2 level in PDM. The MVPA based on an altered amygdala EC pattern yielded a total accuracy of 86.84% for classifying the patients with PDM and HC.

Conclusion

Our study is the first to combine MVPA and EC to explore brain function alteration in PDM. The results could advance understanding of the neural theory of PDM in specifying the pain-free period.

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Data availability

The raw/processed data required to reproduce these findings cannot be shared as the data also form part of an ongoing study.

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Funding

This work was supported by the China National Postdoctoral Program for Innovative Talent (No. BX20190046), the National Natural Science Foundation of China (No. 81973966), the Science and Technology Support Program of Nanchong (19SXHZ0100), and the Doctoral Scientific Research Foundation of North Sichuan Medical College (CBY19-QD10).

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Author notes

  1. Siyi Yu and Liying Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, No. 37 Shierqiao Road, Chengdu, China

    Siyi Yu, Liying Liu, Ling Chen, Menghua Su, Lu Yang, Aijia Li & Xiaojuan Hong

  2. Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Siyi Yu

  3. North Sichuan Medical College, Nanchong, China

    Zhifu Shen

  4. Chengdu Xinan Gynecology Hospital, Chengdu, China

    Wei Wei, Xiaoli Guo & Jie Yang

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  1. Siyi Yu
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  2. Liying Liu
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  3. Ling Chen
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Correspondence to Xiaojuan Hong or Jie Yang.

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The current study was approved by the Institutional Review Board of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (CDUTCM), Chengdu, China. Written informed consent was obtained from each participant before enrollment.

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Yu, S., Liu, L., Chen, L. et al. Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study. Brain Imaging and Behavior 16, 2517–2525 (2022). https://doi.org/10.1007/s11682-022-00707-9

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