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脑网络分析:使用成本整合分离成本与拓扑结构。

Brain network analysis: separating cost from topology using cost-integration.

机构信息

Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom.

出版信息

PLoS One. 2011;6(7):e21570. doi: 10.1371/journal.pone.0021570. Epub 2011 Jul 28.

Abstract

A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.

摘要

一种统计上合理的脑网络分析方法仍然缺乏。由于拓扑结构本质上依赖于布线成本,而成本则定义为无权重图中的边数,因此不同脑网络群体之间的比较很困难。在本文中,我们评估了使用成本综合拓扑指标的好处和限制。我们的重点是比较平均关联权重不同的加权无向图群体,使用全局效率进行比较。我们的主要结果表明,对成本进行积分等同于控制加权图权重集的任何单调变换。也就是说,当对成本进行积分时,我们消除了可能由于权重集的单调变换而导致的拓扑差异。我们的结果适用于任何无权重拓扑度量,以及任何成本水平分布的选择。因此,成本综合有助于将成本差异与拓扑差异区分开来。相比之下,我们表明使用拓扑度量的加权版本通常不是解决此问题的有效方法。事实上,我们证明,在较弱的条件下,使用全局效率的加权版本等同于简单地比较加权成本。因此,我们建议报告(i)加权成本的差异和(ii)成本综合拓扑度量的差异,以及不同成本域分布的差异。我们在 fMRI 工作记忆任务的重新分析中展示了这些技术的应用。我们还提供了一种用于近似成本综合拓扑度量的蒙特卡罗方法。最后,我们讨论了成本综合拓扑的局限性,当某些权重为零时,当权重的秩存在多重性时,以及当人们期望微妙的成本相关拓扑差异时,成本综合可能会掩盖这些差异,这些限制可能会出现问题。

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