Applications of Epsilon Radial Networks in Neuroimage Analyses.
Applications of Epsilon Radial Networks in Neuroimage Analyses.
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DOI：
10.1007/978-3-642-25367-6_21
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Alexander AL
中科院分区：
文献类型：
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作者：
Adluru N;Chung MK;Lange NT;Lainhart JE;Alexander AL
随着扩散核磁共振成像技术的进步和网络分析工具的丰富，“不同人群之间的大脑‘线路’是否不同？”这个问题变得越来越重要。近年来，在扩散张量成像（DTI）的文献中，提出了一种基于神经束成像和epsilon邻域的自动、数据驱动和计算高效的脑网络提取框架。在本文中，我们提出了该框架的新扩展，并展示了这种epsilon径向网络（ERN）在执行各种类型的神经图像分析中的潜在应用。这些扩展使我们不仅可以使用ern来挖掘结构脑网络的拓扑物理特性，还可以以非常有效的方式执行经典的兴趣区域（ROI）分析。因此，我们展示了ern作为基于统计和机器学习分析的新型图像处理镜头的使用。我们展示了它在自闭症研究中的应用，用于识别拓扑和定量组差异，以及进行分类。最后，这些观点并不局限于ern，而是可以有效地使用任何计算效率高的网络提取程序进行人口研究。
“Is the brain ’wiring’ different between groups of populations?” is an increasingly important question with advances in diffusion MRI and abundance of network analytic tools. Recently, automatic, data-driven and computationally efficient framework for extracting brain networks using tractography and epsilon neighborhoods were proposed in the diffusion tensor imaging (DTI) literature. In this paper we propose new extensions to that framework and show potential applications of such epsilon radial networks (ERN) in performing various types of neuroimage analyses. These extensions allow us to use ERNs not only to mine for topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus we demonstrate the use of ERNs as a novel image processing lens for statistical and machine learning based analyses. We demonstrate its application in an autism study for identifying topological and quantitative group differences, as well as performing classification. Finally, these views are not restricted to ERNs but can be effective for population studies using any computationally efficient network-extraction procedures.