Automated analysis of meta-analysis networks
Automated analysis of meta-analysis networks
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DOI：
10.1002/hbm.20135
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发表时间：
2005-05-01
影响因子：
4.8
通讯作者：
Fox, PT
中科院分区：
文献类型：
--
作者：
Lancaster, JL;Laird, AR;Fox, PT
来自基于体素的元分析的大数据集中的高信息含量是复杂的，使得难以容易地解析细节。使用元分析网络作为标准化的数据结构，网络分析算法可以检查复杂的相互关系并解决隐藏的细节。两个新的网络分析算法已被改编为使用元分析网络。第一种称为复制动力学网络分析（rDNA），分析激活的共现，而第二种称为分数相似性网络分析（FSNA），使用二进制模式匹配来形成相似性网络。这两个网络分析方法进行了评估，使用数据激活似然估计（ALE）为基础的Stroop范式的荟萃分析，。对这些数据的两个版本进行了评估，一个使用更严格的ALE阈值（P < 0.01），使用13节点荟萃分析网络，另一个使用更宽松的阈值（P < 0.05），使用22节点网络。为rDNA和FSNA开发了基于Java的应用程序。对rDNA算法进行了修改，为荟萃分析网络提供多个最大或最大集团。用FSNA评估三种不同的相似性度量，以形成节点和实验的子集。rDNA提供了一种方法来衡量元分析的重要性，并补充FSNA，它提供了一个更全面的评估节点相似性子集，实验相似性子集，和整体节点到因素的相似性。在相似性分析中，需要同时使用存在和不存在激活是一项重要发现。FSNA揭示了汇总Stroop荟萃分析的细节，否则需要单独的高度过滤的荟萃分析。这些新的分析工具展示了网络分析策略如何极大地简化和增强基于体素的元分析。(c)2005年威利-利斯。Inc.
The high information content in large data sets from voxel-based meta-analyses is complex, making it hard to readily resolve details. Using the meta-analysis network as a standardized data structure, network analysis algorithms can examine complex interrelationships and resolve hidden details. Two new network analysis algorithms have been adapted for use with meta-analysis networks. The first, called replicator dynamics network analysis (RDNA), analyzes co-occurrence of activations, whereas the second, called fractional similarity network analysis (FSNA), uses binary pattern matching to form similarity subnets. These two network analysis methods were evaluated using data from activation likelihood estimation (ALE)-based meta-analysis of the Stroop paradigm,. Two versions of these data were evaluated, one using a more strict ALE threshold (P < 0.01) with a 13-node meta-analysis network, and the other a more lax threshold (P < 0.05) with a 22-node network. Java-based applications were developed for both RDNA and FSNA. The RDNA algorithm was modified to provide multiple subnets or maximal cliques for meta-analysis networks. Three different similarity measures were evaluated with FSNA to form subsets of nodes and experiments. RDNA provides a means to gauge importance of metanalysis subnets and complements FSNA, which provides a more comprehensive assessment of node similarity subsets, experiment similarity subsets, and overall node-to-factors similarity. The need to use both presence and absence of activations was an important finding in similarity analyses. FSNA revealed details from the pooled Stroop meta-analysis that would otherwise require separate highly filtered meta-analyses. These new analysis tools demonstrate how network analysis strategies can simplify greatly and enhance voxel-based meta-analyses. (c) 2005 Wiley-Liss. Inc.