基于连接组的网瘾症状预测模型。

Qiuyang Feng, Zhiting Ren, Dongtao Wei, Cheng Liu, Xueyang Wang, Xianrui Li, Bijie Tie, Shuang Tang, Jiang Qiu
{"title":"基于连接组的网瘾症状预测模型。","authors":"Qiuyang Feng, Zhiting Ren, Dongtao Wei, Cheng Liu, Xueyang Wang, Xianrui Li, Bijie Tie, Shuang Tang, Jiang Qiu","doi":"10.1093/scan/nsae007","DOIUrl":null,"url":null,"abstract":"<p><p>Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.</p>","PeriodicalId":94208,"journal":{"name":"Social cognitive and affective neuroscience","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10878364/pdf/","citationCount":"0","resultStr":"{\"title\":\"Connectome-based predictive modeling of Internet addiction symptomatology.\",\"authors\":\"Qiuyang Feng, Zhiting Ren, Dongtao Wei, Cheng Liu, Xueyang Wang, Xianrui Li, Bijie Tie, Shuang Tang, Jiang Qiu\",\"doi\":\"10.1093/scan/nsae007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.</p>\",\"PeriodicalId\":94208,\"journal\":{\"name\":\"Social cognitive and affective neuroscience\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10878364/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Social cognitive and affective neuroscience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/scan/nsae007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social cognitive and affective neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/scan/nsae007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

网络成瘾症状(IAS)的特点是持续且不自觉地强迫性使用互联网,从而导致身心健康受到严重损害。本文采用基于连接组的预测建模(CPM)方法,从健康人群的全脑静息态功能连接(rsFC)来解码IAS。研究结果表明,前额叶皮层与小脑和边缘叶之间的功能连接、枕叶与边缘叶和岛叶之间的连接可以预测IAS。所发现的与 IAS 相关的边缘在预测独立样本中的 IAS 方面具有普遍性。此外,我们还发现,与酒精使用障碍症状学(AUDS,与酒精使用障碍相关的一系列症状和行为)的预测网络相比,预测 IAS 的独特贡献网络主要包括涉及枕叶和其他脑叶的连接。目前的数据驱动方法首次证明了基于大脑固有网络组织的IAS大脑预测特征,从而推进了我们对IAD易感性的神经生物学基础的理解,并可能对IAD潜在高危人群的及时干预产生影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Connectome-based predictive modeling of Internet addiction symptomatology.

Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.00
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信