An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks.
An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks.
复制标题
DOI：
10.1016/j.neuroimage.2012.01.071
复制
发表时间：
2012-04-02
期刊：
影响因子：
5.7
通讯作者：
Laurienti, Paul J.
中科院分区：
文献类型：
--
作者：
Simpson, Sean L.;Moussa, Malaak N.;Laurienti, Paul J.
基于群体的大脑连接网络对于有兴趣进一步了解复杂的大脑功能及其在不同精神状态和疾病条件下如何变化的研究人员具有巨大的吸引力。考虑到与解释主体间拓扑变异性相关的困难，准确构建这些网络提出了一项艰巨的挑战。这项任务的可行方法必须产生能够捕获其旨在表示的受试者网络组的本构拓扑特性的网络。传统的方法是使用均值或中值相关网络来体现一组网络。然而，它们的拓扑特性与它们所代表的族群的拓扑特性的一致程度仍有待探索。在这里，我们研究这些均值和中值相关网络的性能。我们还提出了一种基于指数随机图建模框架的替代方法，并将其性能与上述传统方法的性能进行了比较。说明了指数随机图模型（ERGM）在创建捕获单个受试者大脑网络拓扑特征的大脑网络中的效用。然而，它们在产生“代表”一组大脑网络的大脑网络方面的优势还有待检验。在这里，我们表明，我们提出的 ERGM 方法优于传统的基于均值和中值相关性的方法，并为构建基于群体的代表性大脑网络提供了准确而灵活的方法。
Group-based brain connectivity networks have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Accurately constructing these networks presents a daunting challenge given the difficulties associated with accounting for inter-subject topological variability. Viable approaches to this task must engender networks that capture the constitutive topological properties of the group of subjects’ networks that it is aiming to represent. The conventional approach has been to use a mean or median correlation network to embody a group of networks. However, the degree to which their topological properties conform with those of the groups that they are purported to represent has yet to be explored. Here we investigate the performance of these mean and median correlation networks. We also propose an alternative approach based on an exponential random graph modeling framework and compare its performance to that of the aforementioned conventional approach. illustrated the utility of exponential random graph models (ERGMs) for creating brain networks that capture the topological characteristics of a single subject’s brain network. However, their advantageousness in the context of producing a brain network that “represents” a group of brain networks has yet to be examined. Here we show that our proposed ERGM approach outperforms the conventional mean and median correlation based approaches and provides an accurate and flexible method for constructing group-based representative brain networks.
登录
查看更多内容
影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET
影响因子：
3.7
作者：
Joyce KE;Laurienti PJ;Burdette JH;Hayasaka S
通讯作者：
Hayasaka S
影响因子：
1.1
作者：
Rinaldo, Alessandro;Fienberg, Stephen E.;Zhou, Yi
通讯作者：
Zhou, Yi
影响因子：
3
作者：
Alexander-Bloch, Aaron F.;Gogtay, Nitin;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
8.6
作者：
Newman, MEJ
通讯作者：
Newman, MEJ