Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method that rises to the challenges. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell’s identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features from each constituent path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, as an added benefit, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art.

中文翻译：

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NeuroPath2Path：使用路径相似性在神经元柄之间进行分类和弹性变形。

神经元的形状和连通性影响功能。现代成像方法已被证明可以成功地提取形态信息。实现这种形态分析的一种可能途径是通过图论。通过图形编码可以使用高通量信息方法来提取和推断脑功能。然而，图论方法在神经元形态学中的应用在复杂的子图匹配和计算两个成像的时间样本之间的中间形状方面存在困难。在这里，我们报告了一种新颖，有效的图论方法，该方法提出了挑战。神经元的形态由其整体大小，整体形状，局部分支模式和特定于细胞的生物物理特性组成，可随细胞的身份，位置，以及发育和生理状态。已经开发了各种算法来定制基于形状的统计和图形相关特征，以对神经形态进行定量分析，然后使用这些特征对神经元细胞类型进行分类。与从成像或3D重建神经元基于经典特征提取的方法不同，我们提出了一种基于从神经元的体细胞到树突的根路径分解的模型，并从每个组成路径提取形态特征。我们假设测量两个神经元之间的距离可以通过最小化将一个神经元的所有根路径连续变形为另一个的成本来实现。为了验证这一说法，我们首先使用改进的Munkres算法建立两个神经元之间路径的对应关系。下一个，建立使用平方根速度函数的弹性变形框架以执行连续变形，这是一个附加好处，它提供了有效的可视化工具。我们通过实验证明了NeuroPath2Path的功效，NeuroP2P，处于最新状态。