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Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.<\/jats:p>","DOI":"10.1093\/cercor\/bhae034","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T07:23:14Z","timestamp":1705908194000},"source":"Crossref","is-referenced-by-count":8,"title":["Evaluating the depression state during perinatal period by non-invasive scalp EEG"],"prefix":"10.1093","volume":"34","author":[{"given":"Yueheng","family":"Peng","sequence":"first","affiliation":[{"name":"The Clinical Hospital of Chengdu Brain Science Institute , MOE Key Lab for Neuroinformation, , Chengdu 611731 , China"},{"name":"University of Electronic Science and Technology of China , MOE Key Lab for Neuroinformation, , Chengdu 611731 , China"},{"name":"School of Life Science and Technology , Center for 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