Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies. 1. Introduction Insect brains are important model systems for analyzing neural function. This is due to their comparatively simple structure incorporating important brain functions such as sensory information processing, learning, and behavioral control mechanisms [1–3]. Analysis based on the morphologies of neurons and neuropils has greatly promoted the understanding of neural function. In particular, the existence of numerous identified neurons has consolidated the application of insect brains as model neural networks in the field of neuroethology [4, 5]. The detailed morphology of neurons can be captured more readily using recent fluorescence techniques and various genetic technologies in insects [6–9]. These methodological advances have resulted in new insights into brain mechanisms through the use of small and tractable insect brains. A well-known simple insect behavior is the unique orientation to pheromone stimuli displayed by the male silkmoth, Bombyx mori. This programmed behavior triggered by sensing pheromone consists of surge, zigzag, and looping locomotor components [10]. Sensory signal pathways for pheromone have already been identified and characterized by intra- and
References
[1]
S. S. Haupt, T. Sakurai, S. Namiki, T. Kazawa, and R. Kanzaki, “Olfactory information processing in moths,” in The Neurobiology of Olfaction, A. Menini, Ed., chapter 3, CRC Press, 2010.
[2]
M. Hammer and R. Menzel, “Learning and memory in the honeybee,” Journal of Neuroscience, vol. 15, no. 3, pp. 1617–1630, 1995.
[3]
J. D. Armstong and J. I. Van Hemert, “Towards a virtual fly brain,” Philosophical Transactions of the Royal Society A, vol. 367, no. 1896, pp. 2387–2397, 2009.
[4]
C. M. Comer and R. M. Robertson, “Identified nerve cells and insect behavior,” Progress in Neurobiology, vol. 63, no. 4, pp. 409–439, 2001.
[5]
T. Mishima and R. Kanzaki, “Physiological and morphological characterization of olfactory descending interneurons of the male silkworm moth, Bombyx mori,” Journal of Comparative Physiology—A, vol. 184, no. 2, pp. 143–160, 1999.
[6]
S. Hampel, P. Chung, C. E. McKellar, D. Hall, L. L. Looger, and J. H. Simpson, “Drosophila Brainbow: a recombinase-based fluorescence labeling technique to subdivide neural expression patterns,” Nature Methods, vol. 8, no. 3, pp. 253–259, 2011.
[7]
T. Fujiwara, T. Kazawa, S. S. Haupt, and R. Kanzaki, “Ca2+ imaging of identifiable neurons labeled by electroporation in insect brains,” NeuroReport, vol. 20, no. 12, pp. 1061–1065, 2009.
[8]
J. D. Seelig, M. E. Chiappe, G. K. Lott et al., “Two-photon calcium imaging from head-fixed Drosophila during optomotor walking behavior,” Nature Methods, vol. 7, no. 7, pp. 535–540, 2010.
[9]
R. Kanzaki, K. Soo, Y. Seki, and S. Wada, “Projections to higher olfactory centers from subdivisions of the antennal lobe macroglomerular complex of the male silkmoth,” Chemical Senses, vol. 28, no. 2, pp. 113–130, 2003.
[10]
R. Kanzaki, S. Nagasawa, and I. Shimoyama, “Neural basis of odor-source searching behavior in insect microbrain systems evaluated with a mobile robot,” in Bio-Mechanisms of Swimming and Flying, K. Kato, J. Ayers, and H. Morikawa, Eds., pp. 155–169, Springer, Tokyo, Japan, 2004.
[11]
M. Iwano, E. S. Hill, A. Mori et al., “Neurons associated with the flip-flop activity in the lateral accessory lobe and ventral protocerebrum of the silkworm moth brain,” Journal of Comparative Neurology, vol. 518, no. 3, pp. 366–388, 2010.
[12]
K. Rein, M. Z？ckler, M. T. Mader, C. Grübel, and M. Heisenberg, “The Drosophila standard brain,” Current Biology, vol. 12, no. 3, pp. 227–231, 2002.
[13]
H. Otsuna and K. Ito, “Systematic analysis of the visual projection neurons of Drosophila melanogaster. I. Lobula-specific pathways,” Journal of Comparative Neurology, vol. 497, no. 6, pp. 928–958, 2006.
[14]
A. S. Chiang, C. Y. Lin, C. C. Chuang et al., “Three-dimensional reconstruction of brain-wide wiring networks in drosophila at single-cell resolution,” Current Biology, vol. 21, no. 1, pp. 1–11, 2011.
[15]
R. Brandt, T. Rohlfing, J. Rybak et al., “Three-dimensional average-shape atlas of the honeybee brain and its applications,” Journal of Comparative Neurology, vol. 492, no. 1, pp. 1–19, 2005.
[16]
J. Rybak, A. Ku？, H. Lamecker et al., “The digital bee brain: integrating and managing neurons in a common 3D reference system,” Frontiers in Systems Neuroscience, vol. 4, article 30, 2010.
[17]
P. Kvello, B. B. L？faldli, J. Rybak, R. Menzel, and H. Mustaparta, “Digital, three-dimensional average shaped atlas of the Heliothis virescens brain with integrated gustatory and olfactory neurons,” Frontiers in Systems Neuroscience, vol. 3, article 14, 2009.
[18]
W. Huetteroth and J. Schachtner, “Standard three-dimensional glomeruli of the Manduca sexta antennal lobe: a tool to study both developmental and adult neuronal plasticity,” Cell and Tissue Research, vol. 319, no. 3, pp. 513–524, 2005.
[19]
A. E. Kurylas, T. Rohlfing, S. Krofczik, A. Jenett, and U. Homberg, “Standardized atlas of the brain of the desert locust, Schistocerca gregaria,” Cell and Tissue Research, vol. 333, no. 1, pp. 125–145, 2008.
[20]
B. el Jundi, S. Heinze, C. Lenschow, A. Kurylas, T. Rohlfing, and U. Homberg, “The locust standard brain: a 3D standard of the central complex as a platform for neural network analysis,” Frontiers in Systems Neuroscience, vol. 3, article 21, 2009.
[21]
H. Ai, J. Rybak, R. Menzel, and T. Itoh, “Response characteristics of vibration-sensitive interneurons related to Johnston's organ in the honeybee, Apis mellifera,” Journal of Comparative Neurology, vol. 515, no. 2, pp. 145–160, 2009.
[22]
W. Huetteroth, B. el Jundi, S. el Jundi, and J. Schachtner, “3D-reconstructions and virtual 4D-visualization to study metamorphic brain development in the sphinx moth Manduca sexta,” Frontiers in Systems Neuroscience, vol. 4, article 7, 2010.
[23]
T. Kazawa, H. Ikeno, and R. Kanzaki, “Development and application of a neuroinformatics environment for neuroscience and neuroethology,” Neural Networks, vol. 21, no. 8, pp. 1047–1055, 2008.
[24]
A. Jenett, J. E. Schindelin, and M. Heisenberg, “The virtual insect brain protocol: creating and comparing standardized neuroanatomy,” BMC Bioinformatics, vol. 7, article 544, 2006.
[25]
B. Schmid, J. Schindelin, A. Cardona, M. Longair, and M. Heisenberg, “A high-level 3D visualization API for Java and ImageJ,” BMC Bioinformatics, vol. 11, article 274, 2010.
[26]
P. A. Andres and R. A. Isoardi, “Using ITK platform for medical image registration,” Journal of Physics, vol. 90, no. 1, Article ID 012056, 2007.
[27]
A. Rodriguez, D. B. Ehlenberger, P. R. Hof, and S. L. Wearne, “Three-dimensional neuron tracing by voxel scooping,” Journal of Neuroscience Methods, vol. 184, no. 1, pp. 169–175, 2009.
[28]
M. L. Hines and N. T. Carnevale, “NEURON: a tool for neuroscientists,” Neuroscientist, vol. 7, no. 2, pp. 123–135, 2001.
[29]
K. A. Al-Kofahi, A. Can, S. Lasek et al., “Median-based robust algorithms for tracing neurons from noisy confocal microscope images,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 4, pp. 302–317, 2003.
