...
首页> 外文期刊>International Journal of Modern Physics, C. Physics and Computers >Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization
【24h】

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

机译:单个T1加权/ T2加权比率脑网络:小型世界,集线器和模块化组织

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power-law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.
机译:应用网络科学调查复杂系统已成为一个热门话题。在神经科学中,了解复杂脑网络的架构是一个重要的问题。通过功能MRI和结构MRI调查,大脑支持大脑的巨大证据是成本/效率的权衡,通过功能性MRI和结构MRI调查,具有小世界,呼道和模块化组织。然而,T1加权/ T2加权(T1W / T2W)比率脑网络主要是未开发的。在这里,我们利用基于KL发散的方法来构建大规模的单个T1W / T2W比率脑网络,并研究了这些网络的基础拓扑属性。我们的结果支持T1W / T2W比率脑网络由小世界,指数截断的幂律程度分布,额光枢纽和模块化组织组成。此外,网络指标与流体智能之间存在显着的正相关性。因此,T1W / T2W比率脑网络开辟了一种新的途径来理解人类大脑,是未来MRI研究的必要补充。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号