{"title":"神经性贪食症患者大规模大脑网络静息态功能连接的变化:因果分析的证据。","authors":"Jiani Wang, Xinghao Wang, Yiling Wang, Weihua Li, Zhanjiang Li, Lirong Tang, Xinyu Huang, Marcin Grzegorzek, Qian Chen, Zhenchang Wang, Peng Zhang","doi":"10.1093/cercor/bhae430","DOIUrl":null,"url":null,"abstract":"<p><p>Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.</p>","PeriodicalId":9715,"journal":{"name":"Cerebral cortex","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.\",\"authors\":\"Jiani Wang, Xinghao Wang, Yiling Wang, Weihua Li, Zhanjiang Li, Lirong Tang, Xinyu Huang, Marcin Grzegorzek, Qian Chen, Zhenchang Wang, Peng Zhang\",\"doi\":\"10.1093/cercor/bhae430\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.</p>\",\"PeriodicalId\":9715,\"journal\":{\"name\":\"Cerebral cortex\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cerebral cortex\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/cercor/bhae430\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cerebral cortex","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/cercor/bhae430","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
据观察,神经性贪食症(BN)与大规模大脑网络的功能连通性(FC)有关,但其生物学机制仍不清楚。本研究采用双样本孟德尔随机化(MR)方法,以遗传变异作为工具变量(IV),探讨 FC 与 BN 之间的潜在因果关系。研究分析了涉及 2,564 人的全基因组关联研究(GWAS)的汇总数据,以确定遗传预测的 BN。功能磁共振成像参数和材料来自英国生物库。数据库对这些变量进行了独立成分分析处理,以生成最终的 GWAS 数据集。采用了多种方法检测异质性和多向性,包括 MR Pleiotropy RESidual Sum and Outlier、MR Egger 和加权中位数,并以反方差加权作为主要估计方法(P
Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.
Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.
期刊介绍:
Cerebral Cortex publishes papers on the development, organization, plasticity, and function of the cerebral cortex, including the hippocampus. Studies with clear relevance to the cerebral cortex, such as the thalamocortical relationship or cortico-subcortical interactions, are also included.
The journal is multidisciplinary and covers the large variety of modern neurobiological and neuropsychological techniques, including anatomy, biochemistry, molecular neurobiology, electrophysiology, behavior, artificial intelligence, and theoretical modeling. In addition to research articles, special features such as brief reviews, book reviews, and commentaries are included.