【2h】

Defining nodes in complex brain networks

机译:在复杂的大脑网络中定义节点

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摘要

Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10–20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the “correct” method to be used remains an open, possibly unsolvable question that deserves extensive debate and research, we argue that the best method available at the current time is the voxel-wise method.
机译:网络科学为扩大我们对人类大脑在健康,疾病,发育和衰老方面的理解寄予厚望。网络分析正迅速成为分析功能性MRI数据的首选方法。但是,为了优化结果,还需要面对许多技术问题。在功能性大脑网络分析中仍存在争议的一个特定问题是网络节点的定义。在功能性大脑网络中,一个节点代表大脑组织的一些预定义集合,而一条边缘则测量节点对之间的功能连通性。研究人员选择的节点特性在文献中有很大不同。本手稿根据已发表的手稿进行了回顾,并重点介绍了三种定义节点的主要方法的优缺点。通过为每个相等大小的大脑区域(voxel)分配一个节点来构造按体素的网络。然后,将从每个体素记录的fMRI时间序列用于创建功能网络。解剖学方法利用地图集根据脑结构定义节点。对来自解剖区域内所有体素的fMRI时间序列进行平均,然后用于生成网络。功能激活方法依赖于通常来自数据库的传统功能磁共振成像激活研究的数据来识别网络节点。这样的方法从激活图识别峰或质心,以确定节点的位置。然后,将位于激活焦点坐标处的小球体(直径约为10-20毫米)应用于网络分析中使用的数据。然后将球体中所有体素的fMRI时间序列取平均值,然后将所得时间序列用于生成网络。我们试图澄清讨论并使研究复杂的大脑网络向前发展。尽管要使用的“正确”方法仍然是一个悬而未决的问题,值得广泛的辩论和研究,但我们认为,当前可用的最佳方法是基于体素的方法。

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