ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification
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1
Institute of system neuroscience, National Tsing Hua University, Taiwan
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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.
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
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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