Enhanced subject-specific resting-state network detection and extraction with fast fMRI
Enhanced subject-specific resting-state network detection and extraction with fast fMRI
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DOI：
10.1002/hbm.23420
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发表时间：
2017-02-01
影响因子：
4.8
通讯作者：
LeVan, Pierre
中科院分区：
文献类型：
--
作者：
Akin, Burak;Lee, Hsu-Lei;LeVan, Pierre
静息态网络已经成为研究大脑功能的重要工具。一种允许测量大脑功能的超快速成像技术，称为磁共振脑成像（MREG），其时间分辨率比标准回波平面成像（EPI）高一个数量级。这种新的序列有助于纠正生理伪影，提高fMRI分析的灵敏度。在这项研究中，EPI与MREG在提取静止状态网络的能力方面进行了比较。健康对照组进行了两次连续的静息状态扫描，一次使用EPI，另一次使用MREG。分别对两个数据集进行受试者水平独立成分分析（伊卡）。使用斯坦福大学FIND地图集包裹作为网络模板，在每个主题中量化对应于每个网络的伊卡图的存在。在MREG数据集中检测到的单个网络的数量显著高于EPI。此外，使用MREG数据的短时间段，例如50秒，仍然可以检测和跟踪一致的网络。因此，快速功能磁共振成像的结果在一个增加的能力，以提取不同的功能区域在个别受试者的水平相同的扫描时间，也允许提取一致的网络在较短的时间间隔比使用EPI时，这是显着相关的动态功能连接波动的分析。《脑地图》38：817-830，2017年。(c)2016 Wiley Periodicals，Inc.
Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. (c) 2016 Wiley Periodicals, Inc.