Estimation of resting-state functional connectivity using random subspace based partial correlation: a novel method for reducing global artifacts.
Estimation of resting-state functional connectivity using random subspace based partial correlation: a novel method for reducing global artifacts.
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DOI：
10.1016/j.neuroimage.2013.05.118
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发表时间：
2013-11-15
期刊：
影响因子：
5.7
通讯作者：
Menon V
中科院分区：
文献类型：
--
作者：
Chen T;Ryali S;Qin S;Menon V
基于静息状态功能磁共振成像(RsfMRI)的内在功能连通性分析已成为研究脑功能组织的有力工具。生理噪声等全局性伪像在内在功能连通性的估计中是一个重要问题。在这里，我们开发并测试了一种新的用于功能连通性的随机子空间方法(RSMFC)，该方法有效地去除了rsfMRI数据中的全局伪影。RSMFC使用在整个大脑中随机采样的多个体素子集来估计种子区域和每个目标脑体素之间的部分相关性。我们在模拟和实验rsfMRI数据上对RSMFC进行了评估，并将其性能与依赖于全局平均回归(GSReg)的标准方法进行了比较，GSReg是广泛用于去除全局伪影的方法。通过大量的仿真实验，我们证明了RSMFC在去除rsfMRI数据中的全局伪影方面是有效的。关键是，我们使用一个新的模拟数据集来证明，与GSReg不同，RSMFC不会人为地在内在不相关的网络之间引入反相关性，这是对可靠地估计功能连通性至关重要的结果。此外，我们还表明，RSMFC的总体敏感性、特异性和准确性都优于GSReg。对22名健康成年人的rsfMRI实验数据中的后扣带回皮质连通性的分析显示，默认模式网络中具有强大的功能连通性，包括更可靠地识别与GSReg遗漏的左右内侧颞叶区域的连通性。值得注意的是，与GSReg相比，RSMFC与侧额顶区的负相关性明显较弱。我们的结果表明，RSMFC是一种有效的方法，可以最大限度地减少全局伪影和人为负相关性的影响，同时准确地恢复固有的脑功能网络。
Intrinsic functional connectivity analysis using resting-state functional magnetic resonance imaging (rsfMRI) has become a powerful tool for examining brain functional organization. Global artifacts such as physiological noise pose a significant problem in estimation of intrinsic functional connectivity. Here we develop and test a novel random subspace method for functional connectivity (RSMFC) that effectively removes global artifacts in rsfMRI data. RSMFC estimates the partial correlation between a seed region and each target brain voxel using multiple subsets of voxels sampled randomly across the whole brain. We evaluated RSMFC on both simulated and experimental rsfMRI data and compared its performance with standard methods that rely on global mean regression (GSReg) which are widely used to remove global artifacts. Using extensive simulations we demonstrate that RSMFC is effective in removing global artifacts in rsfMRI data. Critically, using a novel simulated dataset we demonstrate that, unlike GSReg, RSMFC does not artificially introduce anti-correlations between inherently uncorrelated networks, a result of paramount importance for reliably estimating functional connectivity. Furthermore, we show that the overall sensitivity, specificity and accuracy of RSMFC are superior to GSReg. Analysis of posterior cingulate cortex connectivity in experimental rsfMRI data from 22 healthy adults revealed strong functional connectivity in the default mode network, including more reliable identification of connectivity with left and right medial temporal lobe regions that were missed by GSReg. Notably, compared to GSReg, negative correlations with lateral fronto-parietal regions were significantly weaker in RSMFC. Our results suggest that RSMFC is an effective method for minimizing the effects of global artifacts and artificial negative correlations, while accurately recovering intrinsic functional brain networks.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
DOI：
10.1109/tpami.2010.186
发表时间：
2011-07-01
影响因子：
23.6
作者：
Hoyle, David C.
通讯作者：
Hoyle, David C.
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
5.7
作者：
Chang, Catie;Liu, Zhongming;Chen, Michael C.;Liu, Xiao;Duyn, Jeff H.
通讯作者：
Duyn, Jeff H.
影响因子：
5.7
作者：
Desjardins, AE;Kiehl, KA;Liddle, PF
通讯作者：
Liddle, PF