Metrics for comparing neuronal tree shapes based on persistent homology
Metrics for comparing neuronal tree shapes based on persistent homology
复制标题
DOI：
10.1371/journal.pone.0182184
复制
发表时间：
2017-08-15
期刊：
影响因子：
3.7
通讯作者：
Wang, Yusu
中科院分区：
文献类型：
--
作者：
Li, Yanjie;Wang, Dingkang;Wang, Yusu
随着越来越多的神经解剖学数据通过诸如neuromorpho的努力得以提供。Org和FlyCircuit。因此，开发计算工具以促进从如此大的数据集中自动发现知识的需求变得更加迫切。一个基本问题是如何最好地比较神经元结构，例如组织和分类大量神经元。我们的目标是开发一个灵活而强大的框架来支持大量神经元结构的有效比较和分类。具体来说，我们建议使用基于拓扑持久性的特征矢量化框架。现有的神经元矢量化方法（即将神经元转换为特征向量以支持有效的比较和/或搜索）通常依赖于形态学信息的统计或总结，例如局部平均或最大扭矩角或分区不对称。这些简单的摘要在编码全局树结构方面能力有限。基于最近在计算拓扑领域发展起来的拓扑持久性概念，我们将每个神经元结构向量化为一个简单而信息丰富的摘要。特别是，每一种感兴趣的信息类型都可以表示为在神经元树上定义的描述符函数，然后将其映射到一个简单的持久性签名。通过考虑神经元上的多个描述符函数，我们的框架可以编码局部和全局树结构，以及其他感兴趣的信息（电生理或动态测量）。由此产生的基于持久性的签名可能比形态计量量的简单统计摘要（如平均值/平均值/最大值）提供更多的信息——实际上，我们表明，使用某个描述符函数将给出一个基于持久性的签名，该签名包含比经典的Sholl分析更多的信息。同时，我们的框架保留了将神经元作为简单欧几里得特征空间中的点的效率，这对于构建有效的搜索或索引结构非常重要。我们提出了初步的实验结果来证明我们基于持续的神经元特征矢量化框架的有效性。
As more and more neuroanatomical data are made available through efforts such as Neuro-Morpho. Org and FlyCircuit. org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities-Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework.