我們利用有連結果蠅protocerebral bridge(PCB)或medulla(MED)腦區的神經細胞來測試這套方法，資料來源為國立清華大學腦科學研究中心。平均起來，一個神經細胞中有85%以上的端點(terminal point)其極性能被正確的辨認。在所有測試的形態特徵中，距細胞本體(soma)的遠近對判斷神經極性最為有用。我們的結果顯示ASNeuPI具可行性，並有發展成一套利用型態骨架辨認神經元極性的半自動化流程之潛力。 The direction of signal transmission is crucial for neural networks. Therefore, the direction of signal flow, which could be provided by identifying neuronal polarity, should be included when we analyze neural networks. However, neuronal polarity is usually identified by biochemical method which is time consuming and might not be an appropriate way to deal with large-scale neural networks.
To solve this problem, we proposed the algorithm for skeleton-based neuronal polarity identification (ASNeuPI). In ASNeuPI, we first morphologically divide a neuron into several substructures, and then extract their morphological features. By applying methods in machine learning, we got an optimal axis providing highest accuracy to serve as the discriminant feature for polarity classification.
We tested this method on neurons innervating protocerebral bridge (PCB) or medulla (MED) in Drosophila. The data were obtained from Brain Research Center, National Tsing Hua University. On average, the polarity of above 85% terminal points in a neuron could be correctly identified. Among all the morphological features tested, the distance to soma is the most useful one. Our results show that ASNeuPI is workable and has the potential to provide a computer-based semi-automatic procedure to predict neuronal polarity from skeleton data.