A method for evaluation of neural structure based on reconstruction and its application to an interneuron in the honeybee brain
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1
Univesity of Hyogo, School of Human Scinece and Environment, Japan
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2
Ludwig-Maximilians-Universität München, Department Biologie II, Germany
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3
Fukuoka University, Department of Earth System Science, Japan
Neuronal structure is strongly related to physiological properties, and thus has a close relation to neural network functions. Various scales of observations and analysis of neuronal structure have been performed to reveal neural morphology and connectivity in the brain. In this study, we present a protocol to evaluate neural morphological characteristics quantitatively based on reconstructions from confocal images. Our protocol combines software tools for image filtering and masking with reconstruction by our original software, SIGEN (Minemoto et al., 2009), which segments and extracts dendritic neural structure. The segmentation software extracts structure from confocal images automatically, and selects the largest continuous object as a main structure of reconstructed neuron. A problem for automated segmentation is the occurrence of fragments not connected to it. SIGEN accounts for this problem by connecting the fragments to the main structure based on two criteria, their volumes and distances to the main structure. Furthermore, spurious segments are removed and dendritic branches are smoothed by averaging. The reconstructed neurons are stored in the SWC format, so their morphological properties can be evaluated and compared by quantitative characterization tool, such as vaa3D (Peng et al., 2014). We applied our protocol to an interneuron in the honeybee brain for comparison of their morphological properties.
Acknowledgements
Supported by the Japan Science and Technology Agency (JST) and the Federal Ministry of Education and Research (Grant 01GQ1116)
References
Minemoto et al., SIGEN: System for Reconstructing Three-Dimensional Structure of Insect Neurons, Proceedings of Asia Simulation Conference 2009 (JSST2009), CDROM, Oct. 2009.
Peng et al., Extensible visualization and analysis for multidimensional images using Vaa3D, Nature Protocols, 9, 193-208, 2014.
Keywords:
neuron morphology,
Honeybee,
Vibration sensitive neuron,
Neuron segmentation,
Morphometrics
Conference:
Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.
Presentation Type:
Poster
Topic:
Digital atlasing
Citation:
Ikeno
H,
Kumaraswamy
A,
Kai
K,
Ai
H,
Rautenberg
PL and
Wachtler
T
(2016). A method for evaluation of neural structure based on reconstruction and its application to an interneuron in the honeybee brain.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2016.
doi: 10.3389/conf.fninf.2016.20.00038
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Received:
29 Apr 2016;
Published Online:
18 Jul 2016.
*
Correspondence:
Prof. Hidetoshi Ikeno, Univesity of Hyogo, School of Human Scinece and Environment, Himeji, Hyogo, 670-0092, Japan, ikeno@shse.u-hyogo.ac.jp