首页> 外文期刊>Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism >Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI
【24h】

Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI

机译:使用动脉自旋标记灌注MRI量化静息状态网络的波动

获取原文
获取原文并翻译 | 示例
           

摘要

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) has been widely used to investigate spontaneous low-frequency signal fluctuations across brain resting state networks. However, BOLD only provides relative measures of signal fluctuations. Arterial Spin Labeling (ASL) MRI holds great potential for quantitative measurements of resting state network fluctuations. This study systematically quantified signal fluctuations of the large-scale resting state networks using ASL data from 20 healthy volunteers by separating them from global signal fluctuations and fluctuations caused by residual noise. Global ASL signal fluctuation was 7.59% +/- 1.47% relative to the ASL baseline perfusion. Fluctuations of seven detected resting state networks vary from 2.96% +/- 0.93% to 6.71% +/- 2.35%. Fluctuations of networks and residual noise were 6.05% +/- 1.18% and 6.78% +/- 1.16% using 4-mm resolution ASL data applied with Gaussian smoothing kernel of 6mm. However, network fluctuations were reduced by 7.77% +/- 1.56% while residual noise fluctuation was markedly reduced by 39.75% +/- 2.90% when smoothing kernel of 12 mm was applied to the ASL data. Therefore, global and network fluctuations are the dominant structured noise sources in ASL data. Quantitative measurements of resting state networks may enable improved noise reduction and provide insights into the function of healthy and diseased brain.
机译:依赖于血氧水平(BOLD)的功能磁共振成像(fMRI)已广泛用于研究整个大脑静息状态网络中的自发性低频信号波动。但是,BOLD仅提供信号波动的相对度量。动脉自旋标记(ASL)MRI在定量测量静止状态网络波动方面具有巨大潜力。这项研究使用来自20名健康志愿者的ASL数据,通过将它们与全局信号波动和残留噪声引起的波动分离开来,系统地量化了大型静止状态网络的信号波动。相对于ASL基线灌注,全球ASL信号波动为7.59%+/- 1.47%。七个检测到的静止状态网络的波动范围从2.96%+/- 0.93%到6.71%+/- 2.35%。使用4mm分辨率的ASL数据和6mm高斯平滑核,网络波动和残留噪声分别为6.05%+/- 1.18%和6.78%+/- 1.16%。但是,当将12 mm的平滑核应用于ASL数据时,网络波动减少了7.77%+/- 1.56%,而残留噪声波动显着减少了39.75%+/- 2.90%。因此,全局和网络波动是ASL数据中主要的结构噪声源。静止状态网络的定量测量可以提高降噪效果,并提供有关健康和患病大脑功能的见解。

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