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Network-Based Analysis Reveals Functional Connectivity Related to Internet Addiction Tendency

机译:基于网络的分析揭示了与互联网成瘾趋势相关的功能连通性

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Preoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills). Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.
机译:对互联网的关注和强迫性使用可以产生负面的心理效果,使得越来越多地被认为是精神障碍。本研究采用基于网络的统计数据来探讨休息的全脑功能联系与个人互联网成瘾程度的程度有关,由自额定问卷索引。我们识别出两个拓扑上有重要的网络,一个具有与互联网成瘾倾向呈正相关的连接,以及一个具有与互联网成瘾趋势负相关的连接。两个网络主要在正面区域互连,这可能反映了认知控制的不同方面的正面区域的变化(即,用于控制互联网使用和游戏技能)。接下来,我们将大脑分类为几个大型区域次组,并发现两个网络中的大多数相对的联系比例对应于包括四电路模型的成瘾的小脑模型。最后,我们观察到具有与互联网成瘾趋势相关的最常规联系的大脑区域复制了瘾文献中经常看到的那些,并且通过我们的互联网成瘾研究的荟萃分析来证实。本研究提供了更好地理解涉及互联网成瘾趋势的大规模网络,并表明临床互联网成瘾水平与与成瘾的临床病例相关的地区和连接相关。

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