Network alignment and similarity reveal atlas-based topological differences in structural connectomes.
Network alignment and similarity reveal atlas-based topological differences in structural connectomes.
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DOI：
10.1162/netn_a_00199
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Deslauriers-Gauthier S
中科院分区：
文献类型：
--
作者：
Frigo M;Cruciani E;Coudert D;Deriche R;Natale E;Deslauriers-Gauthier S
不同大脑区域之间的相互作用可以被建模为一个称为连接体的图，其节点对应于预定义的大脑图谱中的包裹。图的边缘编码了图谱区域之间的轴突连接的强度，其可以通过扩散磁共振成像（MRI）纤维束成像来估计。在这里，我们的目标是提供一个新的角度来选择一个合适的图谱结构连接性研究的问题，通过评估如何稳健的图谱捕获网络拓扑结构在一个同质的队列中的不同主题。我们通过评估连接体的可扩展性来衡量这种鲁棒性，即检索提供高度相似图形的图形匹配的可能性。我们引入两个新的概念。首先，图Jaccard指数（GJI），一种基于集合之间的Jaccard指数的图相似性度量; GJI具有以前方法无法满足的自然数学特性。其次，我们设计了WL-对齐，一种新的技术，用于对齐通过适应Weisfeiler-Leman（WL）图同构测试获得的连接体。我们验证了GJI和WL对齐的数据从人类连接组计划数据库，推断出一个策略，选择一个合适的parcellation结构连接性研究。代码和数据是公开的。我们目前对人类大脑结构的理解的一个重要部分依赖于大脑网络的概念，这是通过观察不同的大脑区域如何相互连接而获得的。在本文中，我们提出了一个策略，选择一个合适的大脑结构连接性研究的分区，利用网络对齐和相似性的概念。为此，我们设计了一种新的加权网络之间的相似性度量，称为图Jaccard指数，以及一种新的网络对齐技术，称为WL对齐。通过评估检索提供高度相似的图形的图形匹配的可能性，我们表明，基于形态和结构的地图集定义的大脑网络，在广泛的分辨率范围内更拓扑鲁棒。
The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas that can be estimated via diffusion magnetic resonance imaging (MRI) tractography. Herein, we aim to provide a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Leman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available. An important part of our current understanding of the structure of the human brain relies on the concept of brain network, which is obtained by looking at how different brain regions are connected with each other. In this paper we present a strategy for choosing a suitable parcellation of the brain for structural connectivity studies by making use of the concepts of network alignment and similarity. To do so, we design a novel similarity measure between weighted networks called graph Jaccard index, and a new network alignment technique called WL-align. By assessing the possibility to retrieve graph matchings that provide highly similar graphs, we show that morphology- and structure-based atlases define brain networks that are more topologically robust across a wide range of resolutions.
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