Unveiling perinatal depression: A dual-network EEG analysis for diagnosis and severity assessment

Yueheng Peng; Bin Lv; Fang Liu; Yuqin Li; Yan Peng; Guangying Wang; Lin Jiang; Baodan Chen; Wenming Xu; Dezhong Yao; Peng Xu; Guolin He; Fali Li

Brain Research Bulletin (Oct 2024)

Unveiling perinatal depression: A dual-network EEG analysis for diagnosis and severity assessment

Yueheng Peng,
Bin Lv,
Fang Liu,
Yuqin Li,
Yan Peng,
Guangying Wang,
Lin Jiang,
Baodan Chen,
Wenming Xu,
Dezhong Yao,
Peng Xu,
Guolin He,
Fali Li

Affiliations

Yueheng Peng: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Bin Lv: Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China; Laboratory of the Key Perinatal Diseases, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
Fang Liu: The Fourth People’s Hospital of Chengdu, Chengdu, Sichuan, China
Yuqin Li: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Yan Peng: Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China; Laboratory of the Key Perinatal Diseases, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
Guangying Wang: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Lin Jiang: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Baodan Chen: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Wenming Xu: Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, China
Dezhong Yao: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Peng Xu: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; Correspondence to: No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China.
Guolin He: Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China; Laboratory of the Key Perinatal Diseases, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Correspondence to: No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China.
Fali Li: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610054, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; Correspondence to: No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China.

Journal volume & issue: Vol. 217
p. 111088

Abstract

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Perinatal depression (PD), which affects about 10–20 percent of women, often goes unnoticed because related symptoms frequently overlap with those commonly experienced during pregnancy. Moreover, identifying PD currently depends heavily on the use of questionnaires, and objective biological indicators for diagnosis has yet to be identified. This research proposes a safe and non-invasive method for diagnosing PD and aims to delve deeper into its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram (EEG) for mothers-to-be and fetuses, we collected the resting-state scalp EEG of pregnant women (with PD/healthy) at the 38th week of gestation. To compensate for the low spatial resolution of scalp EEG, source analysis was first applied to project the scalp EEG to the cortical-space. Afterwards, cortical-space networks and large-scale networks were constructed to investigate the mechanism of PD from two different level. Herein, differences in the two distinct types of networks between PD patients and healthy mothers-to-be were explored, respectively. We found that the PD patients illustrated decreased network connectivity in the cortical-space, while the large-scale networks revealed weaker connections at cerebellar area. Further, related spatial topological features derived from the two different networks were combined to promote the recognition of pregnant women with PD from those healthy ones. Meanwhile, the depression severity at patient level was effectively predicted based on the combined spatial topological features as well. These findings consistently validated that the two kinds of networks indeed played off each other, which thus helped explore the underlying mechanism of PD; and further verified the superiority of the combination strategy, revealing its reliability and potential in diagnosis and depression severity evaluation.

Published in Brain Research Bulletin

ISSN: 1873-2747 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: https://www.sciencedirect.com/journal/brain-research-bulletin

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