Detecting network modules in fMRI time series: a weighted network analysis approach.
Detecting network modules in fMRI time series: a weighted network analysis approach.
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DOI：
10.1016/j.neuroimage.2010.05.047
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发表时间：
2010-10-01
期刊：
影响因子：
5.7
通讯作者：
Poldrack, Russell A.
中科院分区：
文献类型：
--
作者：
Mumford, Jeanette A.;Horvath, Steve;Oldham, Michael C.;Langfelder, Peter;Geschwind, Daniel H.;Poldrack, Russell A.
许多fMRI数据的网络分析首先定义一组区域，从每个区域提取平均信号，然后分析区域之间的相关性。在文献中没有解决的一个基本问题是如何最好地定义网络邻域，在这个邻域上，信号被组合用于网络分析。在这里，我们提出了一种新的无监督方法，用于从fMRI数据中识别紧密相连的体素或模块。这种方法，加权体素共激活网络分析（WVCNA）是基于一种方法，该方法最初是为了在基因网络中找到基因模块而开发的。这种方法不同于fMRI中的许多标准网络方法，因为体素之间的连接是通过连续测量来描述的，而通常体素被认为是连接的或不连接的，这取决于两个体素之间的相关性是否在硬阈值下存活。此外，WVCNA不是简单地使用两两相关来描述两个体素之间的联系，而是依赖于拓扑重叠的度量，它不仅比较两个体素的相关程度，而且还比较这对体素与相同的其他体素的高度相关程度。我们演示了使用WVCNA将大脑分割成一组模块，这些模块可以在同一主题和跨主题的数据中可靠地检测到。此外，我们将WVCNA与ICA进行了比较，发现WVCNA模块与ICA组件具有一些相同的结构，但往往更集中于空间。我们还演示了使用一些WVCNA网络指标来评估体素对模块的隶属关系，以及该体素如何与其他模块相关。最后，我们说明了如何在网络分析中使用WVCNA模块来发现大脑区域之间的连接，并表明它产生了合理的结果。
Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA) is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are, but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel’s membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results.
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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
影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
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