Connectivity subnetwork learning for pathology and developmental variations.
Connectivity subnetwork learning for pathology and developmental variations.
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DOI：
10.1007/978-3-642-40811-3_12
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Verma, Ragini
中科院分区：
文献类型：
--
作者：
Ghanbari, Yasser;Smith, Alex R.;Schultz, Robert T.;Verma, Ragini
关键词：
大脑连接的网络表示提供了一种新的手段来研究病理、发育或衰老引起的大脑变化。这些网络的高维性要求方法不仅能够提取突出这些变异来源的模式，而且能够单独描述它们。在本文中，我们提出了一个统一的框架，学习子网络模式的连接，其投影非负分解成一个重建的基础集，以及，额外的基础集代表发展和群体歧视。为了获得这些组件，我们利用人口的几何分布在连通性空间中，通过使用一个图形理论的计划，施加局部保持属性。此外，主题网络到基集中的投影提供了它的低维表示，这将样本中的不同变异来源分开，促进了变异特定的统计分析。建议的框架适用于自闭症受试者的扩散为基础的连接性的研究。
Network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these networks demands methods that are not only able to extract the patterns that highlight these sources of variation, but describe them individually. In this paper, we present a unified framework for learning subnetwork patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing development and group discrimination. In order to obtain these components, we exploit the geometrical distribution of the population in the connectivity space by using a graph-theoretical scheme that imposes locality-preserving properties. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart the different sources of variation in the sample, facilitating variation-specific statistical analysis. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism.
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