Stochastic geometric network models for groups of functional and structural connectomes.
Stochastic geometric network models for groups of functional and structural connectomes.
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DOI：
10.1016/j.neuroimage.2014.07.039
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发表时间：
2014-11-01
期刊：
影响因子：
5.7
通讯作者：
Mukherjee P
中科院分区：
文献类型：
--
作者：
Friedman EJ;Landsberg AS;Owen JP;Li YO;Mukherjee P
结构和功能连接正在成为研究正常大脑功能和开发各种大脑疾病的新生物标记物的重要工具。与目前主导(非连接组)网络文献的单一网络研究不同，连接组分析通常检查经验网络组，然后将其与标准(随机)网络模型进行比较。目前连接组研究的实践是使用来自社会科学和工程背景的随机网络模型作为比较的基础。然而，这些并不一定最适合于连接的分析，连接通常包含一组非常密切的网络，例如发生在一组对照或一组患有特定疾病的患者身上。本文研究了标准随机模型的重要扩展，使其更好地适用于连接分析，并开发了新的统计拟合方法来解释对象间的差异。这些扩展明确地结合了基于距离和半球间/半球内不对称性的关于网络的几何信息(以补充普通度分布信息)，并利用随机选择的网络密度水平(对于固定阈值网络)来更好地捕捉对象之间的平均连通性的变化。这里介绍的新的统计工具使人们能够通过匹配它们的平均特征和它们之间的变化来比较网络组。一个值得注意的发现是，连接具有高度的“小世界”，而不仅仅是几何和程度方面的考虑。
Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. Current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of networks' density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high “smallworldness” beyond that arising from geometric and degree considerations alone.
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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：
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：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN
影响因子：
3.7
作者：
Alexander-Bloch, Aaron F.;Vertes, Petra E.;Gogtay, Nitin
通讯作者：
Gogtay, Nitin