Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA.
Department of Biology, University of Virginia, Charlottesville, VA, USA.
Neuroinformatics. 2020 Jun;18(3):479-508. doi: 10.1007/s12021-019-09450-x.
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.
神经元的形状和连接性会影响其功能。现代成像方法已被证明能够成功提取形态信息。实现这种形态分析的一种潜在途径是通过图论。通过图进行编码可以使用高通量信息方法来提取和推断大脑功能。然而，将图论方法应用于神经元形态学存在一些挑战，例如复杂子图匹配和计算两个成像时间样本之间中间形状的困难。在这里，我们报告了一种新颖而有效的图论方法，该方法成功地应对了这些挑战。神经元的形态由其整体大小、整体形状、局部分支模式和细胞特异性生物物理特性组成，其会因细胞的身份、位置以及发育和生理状态的不同而发生显著变化。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，以对神经元形态进行定量分析，然后使用这些特征对神经元细胞类型进行分类。与基于图像或 3D 重建神经元的经典特征提取方法不同，我们提出了一种基于从神经元的胞体到树突的有根路径分解的模型，并从每个组成路径中提取形态特征。我们假设通过最小化连续变形一个神经元的所有有根路径集到另一个神经元的成本，可以实现测量两个神经元之间的距离。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。接下来，建立了一个采用平方根速度函数的弹性变形框架来进行连续变形，这作为一个附加的好处，提供了一种有效的可视化工具。我们通过实验证明了 NeuroPath2Path、NeuroP2P 的有效性，优于现有技术。
