SPIN: A Method of Skeleton-Based Polarity Identification for Neurons
- 892 Downloads
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.
KeywordsNeuronal polarity Dendrite Axon Drosophila Automated neural reconstruction Connectome
This work is supported by the National Science Council grants #NSC 101-2311-B-007-008-MY3 and Free Excellent Projects, and by the Aim for the Top University Project of the Ministry of Education, Taiwan. We thank the National Center for High-performance Computing for providing the Flycircuit data; Drs. Ann-Shyn Chiang and Hsiu-Ming Chang for helpful discussion. We also thank Dr. Chih-Yung Lin for providing PB data.
Conflict of Interest
The authors declare that they have no conflict of interests.
- Brown, K. M., Barrionuevo, G., Canty, A. J., Paola, V., Hirsch, J. A., Jefferis, G. S. X. E., et al. (2011). The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics, 9(2–3), 143–157. doi: 10.1007/s12021-010-9095-5.PubMedCrossRefGoogle Scholar
- Campagne, M. V. L., Oestreicher, A. B., Henegouwen, P. M. P. V. B. E., & Gispen, W. H. (1990). Ultrastructural double localization of B-50/GAP43 and synaptophysin (p38) in the neonatal and adult rat hippocampus. Journal of Neurocytology, 19(6), 948–961. doi: 10.1007/BF01186822.CrossRefGoogle Scholar
- Ikeno, H., Kanzaki, R., Aonuma, H., Takahata, M., Mizunami, M., Yasuyama, K., et al. (2008). Development of invertebrate brain platform: Management of research resources for invertebrate neuroscience and neuroethology. In M. Ishikawa, K. Doya, H. Miyamoto, & T. Yamakawa (Eds.), Neural information processing (pp. 905–914). Springer: Berlin. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-69162-4_94.CrossRefGoogle Scholar
- Jang, J.-S. R. (2012). Machine Learning Toolbox. http://mirlab.org/jang/matlab/toolbox/machineLearning. Accessed 12 June 2012.
- Lee, P.-C., Chuang, C.-C., Chiang, A.-S., & Ching, Y.-T. (2012). High-throughput computer method for 3D neuronal structure reconstruction from the image stack of the Drosophila brain and its applications. PLoS Computational Biology, 8(9), e1002658. doi: 10.1371/journal.pcbi.1002658.PubMedCentralPubMedCrossRefGoogle Scholar
- Lin, C.-Y., Chuang, C.-C., Hua, T.-E., Chen, C.-C., Dickson, B. J., Greenspan, R. J., et al. (2013a). A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain. Cell Reports, 3(5), 1739–1753. doi: 10.1016/j.celrep.2013.04.022.PubMedCrossRefGoogle Scholar
- Matus, A., Bernhardt, R., & Hugh-Jones, T. (1981). High molecular weight microtubule-associated proteins are preferentially associated with dendritic microtubules in brain. Proceedings of the National Academy of Sciences of the United States of America, 78(5), 3010–3014.PubMedCentralPubMedCrossRefGoogle Scholar
- Squire, L. R., Berg, D., Bloom, F., Lac, S. du, & Ghosh, A. (2008). Subcellular organization of the nervous system: organelles and their functions. In Fundamental Neuroscience (3rd ed., pp. 59–86). Amsterdam; Boston: Elsevier/Academic Press.Google Scholar
- Wang, T., & Liao, D. (2011). Neuronal morphology classification based on SVM. In Computer Science and Service System (CSSS), 2011 International Conference on (pp. 3344–3347). doi: 10.1109/CSSS.2011.5972187.