Distance-dependent consensus thresholds for generating group-representative structural brain networks
Distance-dependent consensus thresholds for generating group-representative structural brain networks
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DOI：
10.1162/netn_a_00075
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发表时间：
2019-01-01
影响因子：
4.7
通讯作者：
Misic, Bratislav
中科院分区：
文献类型：
--
作者：
Betzel, Richard F.;Griffa, Alessandra;Misic, Bratislav
大规模大脑结构网络编码分布在大脑区域之间的白质连接模式。这些连接模式被认为支持认知过程，当受到损害时，可能导致神经认知缺陷和适应不良行为。研究脑网络组织原理的一种有效方法是从多学科队列中构建群体代表网络。这样做可以放大信噪比，并提供更清晰的大脑网络结构图。在这里，我们表明，目前用于生成稀疏组代表网络的方法高估了网络中存在的短程连接的比例，因此，在广泛的网络统计范围内，无法匹配主题级网络。我们提出了一种保留个体受试者连接长度分布的替代方法。我们已经在以前的论文中使用这种方法来生成组代表网络，尽管到目前为止，它的性能还没有得到适当的基准测试，并与其他方法进行比较。由于这种简单的修改，使用这种方法生成的网络成功地概括了主题级别的属性，通过更好地保留促进大脑整合功能而不是分离功能的特征，优于类似的方法。这里开发的方法为未来研究大规模结构脑网络的基本组织原理和特征提供了希望。来自许多主题的稀疏结构连接数据可以使用适当的平均程序简洁地表示。然而，我们表明，这样做的几个流行程序生成的群体平均网络的统计数据与它们打算表示的主题级网络不同。我们认为，这些差异是由群平均矩阵中短距离连接和远距离连接的过度表达和欠表达引起的。我们提出了一个距离相关的阈值过程，它保留了连接长度分布，从而更好地匹配主题级网络及其统计数据。这些发现为数据驱动的连接体探索性分析提供了信息。
Large-scale structural brain networks encode white matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multisubject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating sparse group-representative networks overestimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. We have used this method in previous papers to generate group-representative networks, though to date its performance has not been appropriately benchmarked and compared against other methods. As a result of this simple modification, the networks generated using this approach successfully recapitulate subject-level properties, outperforming similar approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of large-scale structural brain networks. Author SummarySparse structural connectivity data from many subjects can be succinctly represented using appropriate averaging procedures. We show, however, that several popular procedures for doing so generate group-averaged networks with statistics that are dissimilar from the subject-level networks they are intended to represent. These dissimilarities, we argue, arise from the over- and underexpression of short-range and long-distance connections, respectively, in the group-averaged matrix. We present a distance-dependent thresholding procedure that preserves connection length distributions and consequently better matches subject-level networks and their statistics. These findings inform data-driven exploratory analyses of connectomes.