The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI.
The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI.
复制标题
DOI：
10.3389/fncom.2018.00008
复制
发表时间：
2018
影响因子：
3.2
通讯作者：
Valabregue R
中科院分区：
文献类型：
--
作者：
Gargouri F;Kallel F;Delphine S;Ben Hamida A;Lehéricy S;Valabregue R
静息状态功能性MRI（rs-fMRI）是一种能够测量大脑自发活动的成像技术。功能连接性的测量高度依赖于BOLD信号数据处理的质量。在这项研究中，我们的目的是研究的预处理步骤和它们的应用程序的顺序上的小世界拓扑结构和它们的效率在静息态fMRI数据分析的影响，使用图论。我们应用了最标准的预处理步骤：切片定时，重新对齐，平滑，滤波和tCompCor方法。特别是，我们感兴趣的是预处理如何保持小世界经济属性，以及如何在最小化成本的同时最大化网络的局部和全局效率。我们在54名健康受试者中进行的测试表明，预处理步骤的选择和顺序会影响图形测量。我们发现，csr（我们应用重新排列，平滑和tCompCor作为最后一步）和scr（我们应用重新排列，tCompCor和平滑作为最后一步）策略具有最高的全局效率平均值（例如）。此外，我们发现fscr策略（其中我们应用重新对齐，tCompCor，平滑和滤波作为最后一步）具有最高的平均局部效率（el）值。这些结果证实，功能连接性的图论测量取决于处理步骤的顺序，使用平滑和tCompCor作为全局效率的最终步骤，并使用附加的局部效率过滤，获得最佳结果。
Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step) and the scr (where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency (eg). Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency (el) values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency.
登录
查看更多内容
DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bassett DS;Bullmore E;Verchinski BA;Mattay VS;Weinberger DR;Meyer-Lindenberg A
通讯作者：
Meyer-Lindenberg A
影响因子：
3.7
作者：
Humphries MD;Gurney K
通讯作者：
Gurney K
影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
影响因子：
3.7
作者：
Messé A;Caplain S;Pélégrini-Issac M;Blancho S;Lévy R;Aghakhani N;Montreuil M;Benali H;Lehéricy S
通讯作者：
Lehéricy S
影响因子：
--
作者：
Jo HJ;Gotts SJ;Reynolds RC;Bandettini PA;Martin A;Cox RW;Saad ZS
通讯作者：
Saad ZS