Sisi Zheng, Mingkang Song, Nan Song, Hong Zhu, Xue Li, Dongqing Yin, Shanshan Liu, Yan Zhao, Meng Fang, Yanzhe Ning, Hongxiao Jia
{"title":"drug-naïve去人格化-现实感障碍患者的功能失调大尺度脑网络。","authors":"Sisi Zheng, Mingkang Song, Nan Song, Hong Zhu, Xue Li, Dongqing Yin, Shanshan Liu, Yan Zhao, Meng Fang, Yanzhe Ning, Hongxiao Jia","doi":"10.1186/s12888-025-06497-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Depersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.</p><p><strong>Methods: </strong>A total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within-and between-network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large-scale brain networks and scores of clinical assessments in DPRD patients.</p><p><strong>Results: </strong>Compared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.</p><p><strong>Conclusion: </strong>Overall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":"25 1","pages":"59"},"PeriodicalIF":3.4000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11749103/pdf/","citationCount":"0","resultStr":"{\"title\":\"Dysfunctional large-scale brain networks in drug-naïve depersonalization-derealization disorder patients.\",\"authors\":\"Sisi Zheng, Mingkang Song, Nan Song, Hong Zhu, Xue Li, Dongqing Yin, Shanshan Liu, Yan Zhao, Meng Fang, Yanzhe Ning, Hongxiao Jia\",\"doi\":\"10.1186/s12888-025-06497-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Depersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.</p><p><strong>Methods: </strong>A total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within-and between-network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large-scale brain networks and scores of clinical assessments in DPRD patients.</p><p><strong>Results: </strong>Compared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.</p><p><strong>Conclusion: </strong>Overall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. 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Dysfunctional large-scale brain networks in drug-naïve depersonalization-derealization disorder patients.
Background: Depersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.
Methods: A total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within-and between-network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large-scale brain networks and scores of clinical assessments in DPRD patients.
Results: Compared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.
Conclusion: Overall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.
期刊介绍:
BMC Psychiatry is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of psychiatric disorders, as well as related molecular genetics, pathophysiology, and epidemiology.