Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan.
Neuroinformatics. 2014 Jul;12(3):487-507. doi: 10.1007/s12021-014-9225-6.
Directional signal transmission is essential for neural circuit function and thus for connectomic analysis. The directions of signal flow can be obtained by experimentally identifying neuronal polarity (axons or dendrites). However, the experimental techniques are not applicable to existing neuronal databases in which polarity information is not available. To address the issue, we proposed SPIN: a method of Skeleton-based Polarity Identification for Neurons. SPIN was designed to work with large-scale neuronal databases in which tracing-line data are available. In SPIN, a classifier is first trained by neurons with known polarity in two steps: 1) identifying morphological features that most correlate with the polarity and 2) constructing a linear classifier by determining a discriminant axis (a specific combination of the features) and decision boundaries. Each polarity-undefined neuron is then divided into several morphological substructures (domains) and the corresponding polarities are determined using the classifier. Finally, the result is evaluated and warnings for potential errors are returned. We tested this method on fruitfly (Drosophila melanogaster) and blowfly (Calliphora vicina and Calliphora erythrocephala) unipolar neurons using data obtained from the Flycircuit and Neuromorpho databases, respectively. On average, the polarity of 84-92 % of the terminal points in each neuron could be correctly identified. An ideal performance with an accuracy between 93 and 98 % can be achieved if we fed SPIN with relatively "clean" data without artificial branches. Our result demonstrates that SPIN, as a computer-based semi-automatic method, provides quick and accurate polarity identification and is particularly suitable for analyzing large-scale data. We implemented SPIN in Matlab and released the codes under the GPLv3 license.
定向信号传递对于神经回路功能至关重要，因此也是连接组学分析的关键。信号流的方向可以通过实验确定神经元的极性（轴突或树突）来获得。然而，这些实验技术不适用于现有的神经元数据库，因为这些数据库中没有极性信息。为了解决这个问题，我们提出了 SPIN：一种基于骨架的神经元极性识别方法。SPIN 旨在与具有追踪线数据的大规模神经元数据库一起使用。在 SPIN 中，首先通过两步训练具有已知极性的神经元来训练分类器：1）确定与极性最相关的形态特征，2）通过确定判别轴（特征的特定组合）和决策边界来构建线性分类器。然后，将每个无极性定义的神经元划分为几个形态子结构（域），并使用分类器确定相应的极性。最后，对结果进行评估并返回潜在错误的警告。我们使用来自 Flycircuit 和 Neuromorpho 数据库的果蝇（Drosophila melanogaster）和麻蝇（Calliphora vicina 和 Calliphora erythrocephala）单极神经元的数据对该方法进行了测试。平均而言，每个神经元的末端点中有 84-92%的极性可以被正确识别。如果我们将 SPIN 提供的“干净”数据没有人工分支，则可以实现理想的性能，准确率在 93%到 98%之间。我们的结果表明，作为一种基于计算机的半自动方法，SPIN 提供了快速准确的极性识别，特别适合分析大规模数据。我们在 Matlab 中实现了 SPIN，并在 GPLv3 许可证下发布了代码。
