ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification

Abstract

Event Abstract Back to Event ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification Yi-Hsuan Lee1, Yen-Nan Lin1, 2 and Chung-Chuan Lo1, 2* 1 Institute of system neuroscience, National Tsing Hua University, Taiwan 2 Brain Research Center, Taiwan The direction of signal transmission is crucial for functions of neural networks. Therefore, the direction of signal flow, which is regulated by neuronal polarity, should be included when we analyze neural networks. However, the biochemical method used to identify neuronal polarity is time-consuming and may not be an appropriate strategy for analyzing large-scale neural networks. To address this problem, we proposed an algorithm for skeleton-based neuronal polarity identification (ASNeuPI). In ASNeuPI, we first morphologically divide a neuron into several substructures and for each substructure we extract seventeen morphological features. Next, K-nearest classifier (KNNC) is applied for identifying the most influential feature combination that correlates with the polarity of the training dataset. Finally, we perform linear discriminant analysis (LDA) to generate an optimal axis that provides highest accuracy for polarity identification. The optimal axis is then used to identify polarity of neurons in the testing set. We tested this method on neurons innervating protocerebral bridge (PCB) or medulla (MED) in Drosophila. The neuron skeletons were extracted from data obtained from Brain Research Center, National Tsing Hua University, Taiwan. On average, the polarity of more than 85% terminal points in a neuron could be correctly identified. We tested the maximum performance of ASNeuPI on a clean dataset constructed by manually removing artificial branches resulting from noise in raw images. We found that the average accuracy reaches 95% in the best case. Our results show that, as a computer-based semi-automatic procedure, ASNeuPI provides quick polarity identification and is particularly suitable for analyzing large-scale data. Figure 1 Figure 2 Acknowledgements This work is supported by National Science Council grant #NSC 101-2311-B-007 -008 -MY3 and by Aim for the Top University Project of the Ministry of Education, Taiwan. We also thank National Center for High-performance Computing for providing FlyCircuit database. References Chiang, A.-S., Lin, C.-Y., Chuang, C.-C., Chang, H.-M., Hsieh, C.-H., Yeh, C.-W., Shih, C.-T., Wu, J.-J., Wang, G.-T., Chen, Y.-C., et al. (2010). Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Curr Biol. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21129968 [Accessed December 16, 2010]. Keywords: neuronal polarity, Drosophila, neural imaging, neuron reconstruction, neural networks, Axons, Dendrites Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: General neuroinformatics Citation: Lee Y, Lin Y and Lo C (2013). ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00008 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Chung-Chuan Lo, Institute of system neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan, cclo@life.nthu.edu.tw Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo Google Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo Google Scholar Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo PubMed Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.