, Volume 10, Issue 3, pp 243–267 | Cite as

neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome

  • Oliver SchmittEmail author
  • Peter Eipert
Original Article


neuroVIISAS is a generic platform which allows the integration of neuroontologies, mapping functions for brain atlas development, and connectivity data administration; all of which are required for the analysis of structurally and neurobiologically realistic simulations of networks. What makes neuroVIISAS unique is the ability to integrate neuroontologies, image stacks, mappings, visualizations, analyzes and simulations to use them for modelling and simulations. Based on the analysis of over 2020 tracing studies, atlas terminologies and registered histological stacks of images, neuroVIISAS permits the definition of neurobiologically realistic networks that are transferred to the simulation engine NEST. The analysis on a local and global level, the visualization of connectivity data and the results of simulations offer new possibilities to study structural and functional relationships of neural networks. This paper describes the major components and techniques of how to analyse, visualize and simulate with neuroVIISAS shown on a model network at a coarse CNS level (106 regions, 1566 connections) out of 13681 regions and 134043 connections of the left and right part of the CNS. This network of major components of the left and right hemisphere has small-world properties of the Watts-Strogatz model. Furthermore, synchronized subpopulations, oscillations of rate distributions and a time shift of population activities of the left and right hemisphere were observed in the neurocomputational simulations. In summary, a generic platform has been developed that realizes data-analysis-visualization integration for the exploration of network dynamics on multiple levels.


Nervous systems Brainmapping Neuroimaging Connectivity Terminology Ontology Stereotactic atlas Information retrieval Visualization Data integration Data analysis Mouse brain Rat brain Human brain MRI Cell atlas Modeling Simulation Computational neuroscience 



Anterior commissure


Amygdaloid complex


Aqua destillata


Anteroventral cochlear nucleus


Bed nucleus of the stria terminalis anterior division anteromedial nucleus




neuro Visualization, Information, Image, Simulation and Analysis System


Central canal


Corpus callosum

C. elegans

Caenorhabditis elegans


Central nervous system


Caudate putamen


Interhemispheric fissure


Implicit terminologies


Lateral hypothalamic area


List of terms


Medial preoptic area




Periaqueductal gray


Phosphate buffer


Posterior commissure


Precommissural fornix


Peripeduncular lateral hypothalamus




Region of interest


Substantia nigra


Substantia nigra pars reticulata


Substantia nigra pars compacta


Ventral nucleus of the trapezoid body


Visualization Toolkit



The authors thank Manfred Tasche of the Department of Mathematics of the University of Rostock for sharing his expertise and superior help in preparing the manuscript. We appreciate Andreas Wree of the Department of Anatomy for his critical discussions about the rat and mouse brain neuroanatomy. The authors extend their special thanks to Klaus-Peter Schmitz (Department of Biomedical Engineering, University of Rostock) for the support of the neuroVIISAS project. We thank Frauke Winzer, Susanne Lehmann, Hannah Ormanns, Konstanze Phillip and Richard Kettlitz for their faithful work on extending the database and mappings. We would like especially to thank Heidi Schumann and Christian Tominsky (Department of Computergraphics, University of Rostock) for introducing CGV for visualizing neuronal connectivities and Erik Virtel for realizing motif analysis. Sönke Langner (Baltic Imaging Center, University of Greifswald) supported the MRI measurements. All work was supported by the Faculty of Mathematics and Natural Sciences and of the Faculty of Medicine of the University of Rostock.

Supplementary material

12021_2012_9141_MOESM1_ESM.eps (9.2 mb)
High resolution image file (EPS 9.19 MB)
12021_2012_9141_MOESM2_ESM.eps (15.5 mb)
High resolution image file (EPS 15.5 MB)
12021_2012_9141_MOESM3_ESM.eps (13.2 mb)
High resolution image file (EPS 13.1 MB)
12021_2012_9141_MOESM4_ESM.eps (10.1 mb)
High resolution image file (EPS 10.1 MB)
12021_2012_9141_MOESM5_ESM.eps (32.1 mb)
High resolution image file (EPS 32.0 MB)
12021_2012_9141_MOESM6_ESM.eps (20.4 mb)
High resolution image file (EPS 20.4 MB)
12021_2012_9141_MOESM7_ESM.eps (15.4 mb)
High resolution image file (EPS 15.4 MB)
12021_2012_9141_MOESM8_ESM.eps (18.8 mb)
High resolution image file (EPS 18.7 MB)
12021_2012_9141_MOESM9_ESM.eps (47.8 mb)
High resolution image file (EPS 47.8 MB)
12021_2012_9141_MOESM10_ESM.eps (32.2 mb)
High resolution image file (EPS 32.2 MB)
12021_2012_9141_MOESM11_ESM.tex (26 kb)
(TEX 26.1 kb)


  1. Achacoso, T., & Yamamoto, W. (1992). AY’s neuroanatomy of C. elegans for computation. Boca Raton: CRC Press.Google Scholar
  2. Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, 1–10.CrossRefGoogle Scholar
  3. Albert, R., & Barabasi, A. L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.CrossRefGoogle Scholar
  4. Arenas, A., Fernández, A., & Gómez, S. (2008). A complex network approach to the determination of functional groups in the neural system of C. elegans. Lecture Notes in Computer Science, 5151, 9–18.CrossRefGoogle Scholar
  5. Baltz, A., & Kliemann, L. (2004). Spectral analysis. In U. Brandes & T. Erlebach (Eds.), Network analysis. Lecture notes in computer science (Vol. 3418, pp. 373–416). Springer.Google Scholar
  6. Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12(6), 512–523.PubMedCrossRefGoogle Scholar
  7. Berge, C. (1985). Graphs and hypergraphs. Elsevier Science Ltd.Google Scholar
  8. Bezgin, G., Reid, A. T., Schubert, D., & Kötter, R. (2009). Matching spatial with ontological brain regions using java tools for visualization, database access, and integrated data analysis. Neuroinformatics, 7, 7–22.PubMedCrossRefGoogle Scholar
  9. Bittner, T., Donnelly, M., Goldberg, L., & Neuhaus, F. (2008). Computational Biology Series. Springer, Ch. Modeling principles and methodologies—spatial representation and reasoning (pp. 307–326).Google Scholar
  10. Bjaalie, J. G. (2002). Localization in the brain: New solutions emerging. Nature Reviews. Neuroscience, 3, 322–325.PubMedCrossRefGoogle Scholar
  11. Blinder, P., Baruchi, I., Volman, V., Levine, H., Baranes, D., & Jacob, E. (2005). Functional topology classification of biological computing networks. Natural Computing, 4, 339–361.CrossRefGoogle Scholar
  12. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424, 175–308.CrossRefGoogle Scholar
  13. Bohland, J. W., Wu, C., Barbas, H., Bokil, H., Bota, M., Breiter, H. C., et al. (2009). A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLoS Computational Biology, 5(3), 1–9.CrossRefGoogle Scholar
  14. Bota, M., & Arbib, M. (2004). Integrating databases and expert systems for the analysis of brain structures: connections, similarities, and homologies. Neuroinformatics, 2, 19–58.PubMedCrossRefGoogle Scholar
  15. Bota, M., Dong, H., & Swanson, L. (2005). Brain architecture management system. NeuroImage, 3, 15–48.Google Scholar
  16. Bota, M., & Swanson, L. (2006). A new module for on-line manipulation and display of molecular information in the brain architecture management system. Neuroinformatics, 4, 275–298.PubMedCrossRefGoogle Scholar
  17. Bota, M., & Swanson, L. (2007). Online workbenches for neural network connections. Journal of Comparative Neurology, 500, 807–814.PubMedCrossRefGoogle Scholar
  18. Bota, M., & Swanson, L. (2008). Bams neuroanatomical ontology: Design and implementation. Frontiers in Neuroinformatics, 2, 1–8.CrossRefGoogle Scholar
  19. Bota, M., & Swanson, L. (2010). Collating and curating neuroanatomical nomenclatures: principles and use of the brain architecture knowledge management system (BAMS). Frontiers in Neuroinformatics, 4, 1–16.CrossRefGoogle Scholar
  20. Bowden, D., & Dubach, M. (1995). Neuronames brain hierarchy. NeuroImage, 2, 63–83.PubMedCrossRefGoogle Scholar
  21. Bowden, D., & Dubach, M. (2003). Neuronames 2002. Neuroinformatics, 1, 43–59.PubMedCrossRefGoogle Scholar
  22. Bowden, D., Dubach, M., & Park, J. (2007). Creating neuroscience ontologies. Methods in Molecular Biology, 401, 67–87.PubMedCrossRefGoogle Scholar
  23. Brevik, A., Leergaard, T., Svanevik, M., & Bjaalie, J. (2001). Three-dimensional computerised atlas of the rat brain stem precerebellar system: approaches for mapping, visualization, and comparison of spatial distribution data. Anatomy and Embryology, 204, 319–332.PubMedCrossRefGoogle Scholar
  24. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10, 186–198.PubMedCrossRefGoogle Scholar
  25. Burns, G. (1997). Neural connectivity of the rat: Theory, methods and applications. Ph.D. thesis, university of Oxford.Google Scholar
  26. Burns, G., & Cheng, W.-C. (2006). Tools for knowledge acquisition within the neuroscholar system and their application to anatomical tract-tracing data. Journal of Biomedical Discovery and Collaboration, 1, 10–16.PubMedCrossRefGoogle Scholar
  27. Burns, G., Cheng, W.-C., Thompson, R., & Swanson, L. (2006). The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases. BMC Bioinformatics, 7, 531–550.PubMedCrossRefGoogle Scholar
  28. Burns, G., Cheng, W.-C., Thompson, R., & Swanson, L. (2008a). The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases. Lecture Notes in Computer Science, 5151, 9–18.CrossRefGoogle Scholar
  29. Burns, G., Feng, D., & Hovy, E. (2008b). Studies in computational intelligence (85): Computational intelligence in medical informatics. Springer, Ch. Intelligent approaches to mining the primary research literature: Techniques, systems, and examples (pp. 17–50).Google Scholar
  30. Burns, G., & Young, M. (2000). Analysis of the connectional organization of neural systems associated with the hippocampus in rats. Philosophical Transactions of the Royal Society London. Series B, Biological Sciences, 355, 55–70.CrossRefGoogle Scholar
  31. Canteras, N., Ribeiro-Barbosa, E., Goto, M., Cipolla-Neto, J., & Swanson, L. (2011). The retinohypothalmic tract: comparison of axonal projection patterns from four major targets. Brain Research Reviews, 65, 150–183.PubMedCrossRefGoogle Scholar
  32. Carson, J., Ju, T., Lu, H., Thaller, C., Xu, M., Pallas, S. et al. (2005). A digital atlas to characterize the mouse brain transcriptome. PLoS Computational Biology, 1, 289–296.CrossRefGoogle Scholar
  33. Casati, R., & Varzi, A. (1999). Parts and places. Cambridge: The MIT Press.Google Scholar
  34. Catmull, E., & Clark, J. (1978). Recursively generated b-spline surfaces on arbitrary topological meshes. Computer-Aided Design, 10, 350–355.CrossRefGoogle Scholar
  35. Chana, E., KovacevÃņcb, N., Hoa, S., Henkelmanb, R., & Hendersona, J. (2007). Development of a high resolution three-dimensional surgical atlas of the murine head for strains 129s1/svimj and c57bl/6j using magnetic resonance imaging and micro-computed tomography. Neuroscience, 144, 604–615.CrossRefGoogle Scholar
  36. Cheng, C. Y., Huang, C. Y., & Sun, C. T. (2008). Mining bridge and brick motifs from complex biological networks for functionally and statistically significant discovery. IEEE Transactions on Systems, Man and Cybernetics. Part B. Cybernetics, 38, 17–24.CrossRefGoogle Scholar
  37. Cimino, J., & Zhu, X. (2006). The practical impact of ontologies on biomedical informatics. Yearbook of Medical Informatics, 2006, 200–211.Google Scholar
  38. da Costa, L. F., Rodrigues, F. A., Travieso, G., & Boas, P. V. (2007). Characterization of complex networks: A survey of measurements. Advances in Physics, 56, 167–242.CrossRefGoogle Scholar
  39. da Costa, F. L., & Sporns, O. (2006). Correlating thalamocortical connectivity and activity. Applied Physics Letters, 89, 1–3.Google Scholar
  40. Day-Wilson, K., Jones, D., Southam, E., Ciliab, J., & Totterdell, S. (2006). Medial prefrontal cortex volume loss in rats with isolation rearing-induced deficits in prepulse inhibition of acoustic startle. Neuroscience, 141, 1113–1121.PubMedCrossRefGoogle Scholar
  41. Deleus, F., & Hulle, M. V. (2004). Modelling the connectivity between terms in the neuroscience literature. In Proceedings of 2004 IEEE international joint conference on neural networks (Vol. 4. pp. 3293–3296).Google Scholar
  42. Dinov, I., Valentino, D., Shin, B., Konstantinidis, F., Hu, G., MacKenzie-Graham, A., et al. (2006). Loni visualization environment. Journal of Digital Imaging, 19, 148–158.PubMedCrossRefGoogle Scholar
  43. Dong, J., & Horvath, S. (2007). Understanding network concepts in modules. BMC Systems Biology, 1, 1–20.CrossRefGoogle Scholar
  44. Dong, S., Bremer, P.-T., Garland, M., Pascucci, V., & Hart, J. (2006). Spectral surface quadrangulation. ACM Transactions on Graphics, 25, 1057–1066.CrossRefGoogle Scholar
  45. Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F. (2008). Critical phenomena in complex networks. Reviews of Modern Physics, 80, 1275–61.CrossRefGoogle Scholar
  46. Echtermeyer, C., da Costa, F. L., Rodrigues, F., & Kaiser, M. (2011). Automatic network fingerprinting through single-node motifs. Plos One, 6, e15765 1–9.Google Scholar
  47. Eichele, G., Chiu, W., Thaller, C., Armstrong, D., Carson, J., Lu, H.-C., et al. (2009). The mouse brain library.
  48. Estrada, E., & Hatano, N. (2009). Communicability graph and community structures in complex networks. Applied Mathematics and Computation, 214, 500–511.CrossRefGoogle Scholar
  49. Felleman, D., & Essen, D. V. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex, 1, 1–47.PubMedCrossRefGoogle Scholar
  50. Feng, D., Burns, G., & Hovy, E. (2007). Extracting data records from unstructured biomedical full text. In Proceedings of the 2007 joint conference on emperical methods in natural language processing and computational natural language learning (pp. 837–846).Google Scholar
  51. French, L., Lane, S., Xu, L., & Pavlidis, P. (2009). Automated recognition of brain region mentions in neuroscience literature. Frontiers in Neuroinformatics, 3, 1–7.CrossRefGoogle Scholar
  52. G. Paxinos, C. W. (2009). BrainNavigator. Academic Press Inc.Google Scholar
  53. Gallyas, F., Hsu, M., & Buzsaki, G. (1993). Four modified silver methods for thick sections of formaldehyde-fixed mammalian central nervous tissue: ’dark’ neurons, perikarya of all neurons, microglial cells and capillaries. Journal of Neuroscience Methods, 50, 159–164.PubMedCrossRefGoogle Scholar
  54. Gewaltig, M.-O., & Diesmann, M. (2007). Nest (neural simulation tool). Scholarpedia, 2(4), 1430.CrossRefGoogle Scholar
  55. Gleeson, P., Steuber, V., & Silver, R. (2007). neuroconstruct: A tool for modeling networks of neurons in 3d space. Neuron, 54, 219–235.PubMedCrossRefGoogle Scholar
  56. Goodhill, G., Simmen, M., & Willshaw, D. (1995). An evaluation of the use of multidimensional scaling for understanding brain connectivity. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 348, 265–280.PubMedCrossRefGoogle Scholar
  57. Goryczka, S., & Arodź, T. (2006). Complex-network-based methodology for analysis of biomedical data. Bio-Algorithms and Med-Systems, 3, 19–26.Google Scholar
  58. Gustafson, C., Bug, W., & Nissanov, J. (2007). Neuroterrain—a client-server system for browsing 3d biomedical image data sets. BMC Bioinformatics, 8(40), 1–15.Google Scholar
  59. Gustafson, C., Tretiak, O., Bertrand, L., & Nissanov, J. (2004). Design and implementation of software for assembly and browsing of 3d brain atlases. Computer Methods and Programs in Biomedicine, 74, 53–61.PubMedCrossRefGoogle Scholar
  60. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., et al. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6, 1–15.CrossRefGoogle Scholar
  61. Hilgetag, C.-C., Burns, G., O’Neill, M., Scannell, J., & Young, M. (2000a). Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 355, 91–110.PubMedCrossRefGoogle Scholar
  62. Hilgetag, C.-C., & Grant, S. (2000). Uniformity, specifity and variability of corticocortical connectivity. Phil. Trans. R. Soc. Lond. B, 355, 7–20.CrossRefGoogle Scholar
  63. Hilgetag, C.-C., & Kaiser, M. (2004). Clustered organization of cortical connectivity. Neuroinformatics, 2, 353–360.PubMedCrossRefGoogle Scholar
  64. Hilgetag, C. C., O’Neill, M. A., & Young, M. P. (1996). Indeterminate organization of the visual system. Science, 271, 776–777.PubMedCrossRefGoogle Scholar
  65. Hilgetag, C. C., O’Neill, M. A., & Young, M. P. (2000b). Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor. Phil. Trans. R. Soc. Lond. B, 355, 71–89.CrossRefGoogle Scholar
  66. Hjornevik, T., Leergaard, T., Darine, D., Moldestad, O., Dale, A., Willoch, F., et al. (2007). Three-dimensional atlas system for mouse and rat brain imaging data. Front. Neuroinf., 1, 1–11.Google Scholar
  67. Honey, C., Kötter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104, 10240–10245.PubMedCrossRefGoogle Scholar
  68. Honey, C. J., & Sporns, O. (2008). Dynamical consequences of lesions in cortical networks. Human Brain Mapping, 29(7), 802–809.PubMedCrossRefGoogle Scholar
  69. Hovakimyan, M., Haas, S.-P., Schmitt, O., Gerber, B., Wree, A., & Andressen, C. (2008). Mesencephalic human neural progenitor cells transplanted into the adult hemiparkinsonian rat striatum lack dopaminergic differentiation but improve motor behavior. Cells Tissues Organs, 188, 373–383.PubMedCrossRefGoogle Scholar
  70. Humphries, M., Gurney, K., & Prescott, T. (2006). The brainstem reticular formation is a small-world, not scale-free, network. Proc. R. Soc. B, 273, 503–511.PubMedCrossRefGoogle Scholar
  71. Ju, T., Warrena, J., Carsonf, J., Bellod, M., Kakadiarisd, I., Chiub, W., et al. (2006). 3d volume reconstruction of a mouse brain from histological sections using warp filtering. Journal of Neuroscience Methods, 156, 84–100.PubMedCrossRefGoogle Scholar
  72. Kachlik, D., Baca, V., Bozdechova, I., Cech, P., & Musil, V. (2008). Anatomical terminology and nomenclature: Past, present and highlights. Surgical and Radiologic Anatomy, 30, 459–466.PubMedCrossRefGoogle Scholar
  73. Kammer, F., & Täubig, H. (2004). Connectivity. In: U. Brandes & T. Erlebach (Eds.), Network analysis. Lecture notes in computer science (Vol. 3418, pp. 143–177). Springer.Google Scholar
  74. Keim, D., Mansmann, F., Schneidewind, J., Jim, T., & Ziegler, H. (2008). Visual Analytics: Scope and challenges. Universität Konstanz.Google Scholar
  75. Keinan, A., Hilgetag, C. C., Meilijson, I., & Ruppin, E. (2004). Causal localization of neural function: The shapley value method. Neurocomputing, 58–60, 215–222.CrossRefGoogle Scholar
  76. Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M., et al. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration. NeuroImage, 46, 786–802.PubMedCrossRefGoogle Scholar
  77. Kosara, R. (2007). Visual analytics (Vol. ITCS 4122/5122).Google Scholar
  78. Kötter, R. (2002). Neuroscience databases—a practical guide. Norwell, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
  79. Kötter, R. (2004). Online retrieval, processing, and visualization of primate connectivity data from the cocomac database. Neuroinformatics, 2, 127–144.PubMedCrossRefGoogle Scholar
  80. Kötter, R., Hilgetag, C., & Stephan, K. (2001). Connectional characteristics of areas in Walker’s map of primate prefrontal cortex. Neurocomputing, 38–40, 741–746.CrossRefGoogle Scholar
  81. Kötter, R., Reid, A. T., Krumnack, A., Wanke, E., & Sporns, O. (2007). Shapley ratings in brain networks. Frontiers in Neuroinformatics, 1, 1–9.Google Scholar
  82. Kötter, R., & Stephan, K. E. (2003). Network participation indices: characterizing component roles for information processing in neural networks. Neural networks: The official Journal of the International Neural Network Society, 16(9), 1261–1275.CrossRefGoogle Scholar
  83. Lee, J. T., Munch K. R., C. J. P. J. (2008). Internet image viewer (iiv). BMC Medical Imaging, 29, 1–20.Google Scholar
  84. Lein, E., Hawrylycz, M., Ao, N., Ayres, M., Bensinger, A., Bernard, A., et al. (2007). Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445, 168–176.PubMedCrossRefGoogle Scholar
  85. Li, C., Kao, C.-Y., Gore, J., & Ding, Z. (2008). Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 17, 1940–1949.PubMedCrossRefGoogle Scholar
  86. Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., et al. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5, 1–17.Google Scholar
  87. Lillehaug, S., Øyan, D., Leergaard, T., & Bjaalie, J. (2002). Comparison of semi-automatic and automatic data acquisition methods for studying three-dimensional distributions of large neuronal populations and axonal plexuses. Network: Computation in Neural Systems, 13, 343–356.CrossRefGoogle Scholar
  88. Lohmann, K., Gundelfinger, E., Scheich, H., Grimm, R., Tischmeyer, W., Richter, K., et al. (1998). Brainview: A computer program for reconstruction and interactive visualization of 3d data sets. Journal of Neuroscience Methods, 84, 143–154.PubMedCrossRefGoogle Scholar
  89. MacDonald, N. (1983). Trees and networks in biological models. Wiley Ltd.Google Scholar
  90. MacKenzie-Graham, A., Lee, E., Dinov, I., Bota, M., Shattuck, D., Ruffins, S., et al. (2004). A multimodal, multidimensional atlas of the C57BL/6J mouse brain. Journal of Anatomy, 204, 93–102.PubMedCrossRefGoogle Scholar
  91. Martin, R., Bowden, D., 1996. A stereotaxic template atlas of the macaque brain for digital imaging and quantitative neuroanatomy. NeuroImage 4, 119–150.PubMedCrossRefGoogle Scholar
  92. Martone, M. E., Gupta, A., & Ellisman, M. H. (2004). E-neuroscience: challenges and triumphs in integrating distributed data from molecules to brains. Nature Neuroscience, 7, 467–472.PubMedCrossRefGoogle Scholar
  93. Martone, M. E., Tran, J., Wong, W. W., Sargis, J., Fong, L., Larson, S., et al. (2008). The cell centered database project: An update on building community resources for managing and sharing 3d imaging data. Journal of Structural Biology, 161, 220–231.PubMedCrossRefGoogle Scholar
  94. Merker, B. (1983). Silver staining of cell bodies by means of physical development. Journal of Neuroscience Methods, 9, 235–241.PubMedCrossRefGoogle Scholar
  95. Milenković, T., Lai, J., & Pržulj, N. (2008). Graphcrunch: A tool for large network analyses. BMC Bioinformatics, 9, 1–11.CrossRefGoogle Scholar
  96. Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., Shen-Orr, S., Ayzenshtat, I., et al. (2004). Superfamilies of evolved and designed networks. Science, 303, 1538–1542.PubMedCrossRefGoogle Scholar
  97. Modersitzki, J. (2004). Numerical methods for image registration. Oxford University Press.Google Scholar
  98. Modersitzki, J. (2009). FAIR; Flexible algorithms for image registration. SIAM, Philadelphia.Google Scholar
  99. Modha, D., & Singh, R. (2010). Network architecture of the long-distance pathways in the macaque brain. PNAS, 107, 13485–13490.PubMedCrossRefGoogle Scholar
  100. Moene, I., Subramaniam, S., Darin, D., Leergaard, T., & Bjaalie, J. (2007). Toward a workbench for rodent brain image data: Systems architecture and design. Neuroinformatics, 5, 35–58.PubMedGoogle Scholar
  101. Musen, M., Noy, N., O’Connor, M., Redmond, T., Rubin, D., Tu, S., et al. (2009). Protégé.
  102. Nagyessy, L., Nepusz, T., Kocsis, L., & Bazso, F. (2006). Prediction of the main cortical areas and connections involved in the tactile function of the visual cortex by network analysis. The European Journal of Neuroscience, 23, 1919–1930.CrossRefGoogle Scholar
  103. Nattkemper, T. (2001). A neural network-based system for high throughput fluorescence micrograph evaluation. Ph.D. thesis, Technical faculty, University of Bielefeld.Google Scholar
  104. Neuhaus, F., & Smith, B. (2008). Computational Biology Series. Springer, Ch. Modeling principles and methodologies—relations in anatomical ontologies (pp. 289–305).Google Scholar
  105. Newman, M. (2006). Modularity and community strucutre in networks. PNAS, 103, 8577–8582.PubMedCrossRefGoogle Scholar
  106. Newman, M., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review, 69, 1–15.Google Scholar
  107. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.CrossRefGoogle Scholar
  108. Ng, L., Bernard, A., Lau, C., Overly, C., Dong, H., Kuan, C., et al. (2009). An anatomic gene expression atlas of the adult mouse brain. Nature Neuroscience, 12, 356–362.PubMedCrossRefGoogle Scholar
  109. Ng, L., Pathak, S., Kuan, C., Lau, C., Dong, H.-W., Sodt, A., et al. (2007). Neuroinformatics for genome-wide 3-d gene expression mapping in the mouse brain. IEEE Transactions on Computational Biology and Bioinformatics, 4, 382–393.PubMedCrossRefGoogle Scholar
  110. O’Neill, M., & Hilgetag, C. (2001). The portable unix programming system (pups) and cantor: a computational environment for dynamical representation and analysis of complex neurobiological data. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356, 1259–1276.PubMedCrossRefGoogle Scholar
  111. Palombi, O., Shin, J.-W., Watson, C., & Paxinos, G. (2006). Neuroanatomical affiliation visualization-interface system. Neuroinformatics, 4, 299–317.PubMedCrossRefGoogle Scholar
  112. Paxinos, G., & Watson, C. (2007). The rat brain in stereotaxic coordinates (6th ed.). Amsterdam: Elsevier Academic Press.Google Scholar
  113. Poliakov, A., Hertzenberg, X., Moore, E., Corina, D., Ojemann, G., & Brinkley, J. (2007). Unobtrusive integration of data management with fMRI analysis. Neuroinformatics, 5, 3–10.PubMedGoogle Scholar
  114. Rist, S. (1999). A method for classification of biological neurons by means of artificial neuronal networks (in german). Tech. rep., University of Lübeck, Department of Mathematics.Google Scholar
  115. Rosse, C., & Mejino, J. (2003). A reference ontology for biomedical informatics: the foundational model of anatomy. Journal of Biomedical Informatics, 36, 478–500.PubMedCrossRefGoogle Scholar
  116. Rosse, C., & Mejino, J. (2008). Computational Biology Series. Springer, Ch. The foundational model of anatomy ontology (pp. 59–117).Google Scholar
  117. Rubin, D., Talos, I.-F., Halle, M., Musen, M., & Kikinis, R. (2009). Computational neuroanatomy: ontology-based representation of neural components and connectivity. BMC Bioinformatics, 10, 1–8.CrossRefGoogle Scholar
  118. Scannell, J., & Young, M. (1993). The connectional organization of neural systems in the cat cerebral cortex. Current Biology, 3, 191–200.PubMedCrossRefGoogle Scholar
  119. Scannell, J. W., Blakemore, C., & Young, M. P. (1995). Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience, 15, 1463–1483.PubMedGoogle Scholar
  120. Scannell, J. W., Burns, G. A. P. C., Hilgetag, C. C., O’Neil, M. A., & Young, M. P. (1999). The connectional organization of the corticothalamic system of the cat. Cerebral Cortex, 9, 277–299.PubMedCrossRefGoogle Scholar
  121. Schmitt, O., Bethke, S., Sobe, P., Prehn, S., & Maehle, E. (2008). Parallelized segmentation of a serially sectioned whole human brain. Image and Vision Computing, 26, 289–301.CrossRefGoogle Scholar
  122. Schmitt, O., & Birkholz, H. (2010). A hybrid approach to quantify lamination of the cerebral cortex. Int. J. Nonlin. Sci. Sim., 10, 1655–1661.Google Scholar
  123. Schmitt, O., & Eipert, P. (2011). Spike distributions of a population based hierarchical network of the rat amygdaloid complex. BMC Neuroscience, 12(Suppl 1), 1–2.CrossRefGoogle Scholar
  124. Schmitt, O., & Hasse, M. (2008). Radial symmetries based decomposition of cell clusters in binary and gray level images. Pattern Recognition, 41, 1905–1923.CrossRefGoogle Scholar
  125. Schmitt, O., & Hasse, M. (2009). Multiscale morphological decomposition of cell clusters. Computer Vision and Image Understanding, 113, 188–201.CrossRefGoogle Scholar
  126. Schmitt, O., Modersitzki, J., Heldmann, S., Wirtz, S., & Fischer, B. (2007). Image registration of sectioned brains. International Journal of Computer Vision, 73,(1), 5–39.CrossRefGoogle Scholar
  127. Schmitt, O., & Reetz, S. (2009). On the decomposition of cell clusters. Journal of Mathematical Imaging and Vision, 33, 85–103.CrossRefGoogle Scholar
  128. Schmitt, O., Usunoff, K., Lazarov, N., Itzev, D., Eipert, P., Rolfs, A., et al. (2011). Orexinergic innervation of the extended amygdala and basal ganglia in the rat. Brain Structure & Function. doi: 10.1007/s00429-011-0343-8 Google Scholar
  129. Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85, 461–464.PubMedCrossRefGoogle Scholar
  130. Schroeder, W., Martin, K., & Lorensen, B. (2006). The Visualization Toolkit: An object-oriented approach to 3D graphics (4th ed.). Kitware, Inc.Google Scholar
  131. Simmonsa, D., & Swanson, L. (2008). High-resolution paraventricular nucleus serial section model constructed within a traditional rat brain atlas. Neuroscience Letters, 438, 85–89.CrossRefGoogle Scholar
  132. Smith, B., Ceusters, W., Klagges, B., Köhler, J., Kumar, A., Lomax, J., et al. (2005). Relations in biomedical ontologies. Genome Biology, 6, R46.1–15.CrossRefGoogle Scholar
  133. Sporns, O., Honey, C. J., & Kötter, R. (2007). Identification and classification of hubs in brain networks. PLoS One, 2(10), 1–14.CrossRefGoogle Scholar
  134. Sporns, O., & Kötter, R. (2004). Motifs in brain networks. PLoS Biology, 2(11), 1910–1918.CrossRefGoogle Scholar
  135. Sporns, O., Tononi, G., & Edelman, G. M. (2000). Connectivity and complexity: The relationship between neuroanatomy and brain dynamics. Neural Networks, 13(8–9), 909–922.PubMedCrossRefGoogle Scholar
  136. Sporns, O., Tononi, G., & Edelman, G. M. (2002). Theoretical neuroanatomy and the connectivity of the cerebral cortex. Behavioural Brain Research, 135(1–2), 69–74.PubMedCrossRefGoogle Scholar
  137. Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1, 245–251.CrossRefGoogle Scholar
  138. Sporns, O., & Zwi, J. (2004). The small world of the cerebral cortex. NeuroInformatics, 2(2), 145–162.PubMedCrossRefGoogle Scholar
  139. Stam, C. J., & Reijneveld, J. C. (2007). Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics, 1, 1–19.CrossRefGoogle Scholar
  140. Stephan, K., Kamper, L., Bozkurt, A., Burns, G., Young, M., & Kötter, R. (2001). Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac). Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356, 1159–1186.PubMedCrossRefGoogle Scholar
  141. Stephan, K. E., Hilgetag, C. C., Burns, G. A. P. C., O’Neill, M. A., Young, M. P., & Kötter, R. (2000). Computational analysis of functional connectivity between areas of primate cerebral cortex. Phil. Trans. Royal Soc. London, Series B, 355, 111–126.CrossRefGoogle Scholar
  142. Strogatz, S. H. (2001). Exploring complex networks. Nature, 410, 268–276.PubMedCrossRefGoogle Scholar
  143. Swanson, L. (1998). Brain Maps: Structure of the rat brain. A laboratory guide with printed and electronic templates for data, models and schematics. Elsevier.Google Scholar
  144. Swanson, L. (2003). Brain maps: Vol 3: Structure of the rat brain. Elsevier.Google Scholar
  145. Thomas, J., & Cook, K. (2005). Illuminating the path: The R&D agenda for visual analytics. National Visualization and Analytics Center.Google Scholar
  146. Thompson, C., Pathak, S., Jeromin, A., Ng, L., MacPherson, C., Mortrud, M., et al. (2008). Genomic anatomy of the hippocampus. Neuron, 60, 1010–1021.PubMedCrossRefGoogle Scholar
  147. Thompson, R., & Swanson, L. (2010). Hypothesis-driven structural connectivity analysis supports network over hierarchical model of brain architecture. PNAS, 107, 15235–15239.PubMedCrossRefGoogle Scholar
  148. Tominski, C., Abello, J., & Schumann, H. (2009). CGV—an interactive graph visualization system. Computer & Graphics, 33, 660–678.CrossRefGoogle Scholar
  149. Tononi, G., & Sporns, O. (2003). Measuring information integration. BMC Neuroscience, 4, 1–20.CrossRefGoogle Scholar
  150. Tononi, G., Sporns, O., & Edelman, G. (1994). A measure for brain complexity: Relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences, 91, 5033–5037.CrossRefGoogle Scholar
  151. Voloshin, V. (2009). Introduction to graph and hypergraph theory. Nova Science Publishers Inc.Google Scholar
  152. Vulpen, E. V., & Kooy, D. V. D. (1996). Differential maturation of cholinergic interneurons in the striatal patch versus matrix compartments. Journal of Comparative Neurology, 365, 683–691.PubMedCrossRefGoogle Scholar
  153. Warren, J., & Weimer, H. (2001). Subdivision methods for geometric design: A constructive approach. Danvers: Wiley.Google Scholar
  154. White, J., Southgate, E., Thompson, J., & Brenner, S. (1986). The structure of the nervous system of the nematode caenorhabditis elegans. Phil. Trans. Royal Soc. London Series B, 314, 1–340.CrossRefGoogle Scholar
  155. Whitmore, I. (1998). Terminologia anatomica. International anatomical terminology. Stuttgart: Thieme Verlag.Google Scholar
  156. Williams, R. (2003). The mouse brain library.
  157. Wong, P., & Thomas, J. (2004). Visual analytics. IEEE Computer Graphics and Applications, 24, 20–21.PubMedCrossRefGoogle Scholar
  158. Wong, P. C., Foote, H., Chin, G., Mackey, P., & Perrine, K. (2006a). Graph signatures for visual analytics. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1399–1413.PubMedCrossRefGoogle Scholar
  159. Wong, P. C., Foote, H., Mackey, P., & Perrine, K., Chin, G. (2006b). Generating graphs for visual analytics through interactive sketching. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1386–1398.PubMedCrossRefGoogle Scholar
  160. Wree, A., Lutz, B., & Thole, U. (1992). Volumes of the cytoarchitectonic areas in the rat cerebral cortex. Journal für Hirnforschung, 33, 545–548.PubMedGoogle Scholar
  161. Young, M. (1992). Objective analysis of the topological organization of the primate cortical visual system. Nature, 358, 152–155.PubMedCrossRefGoogle Scholar
  162. Young, M. (1993). The organization of neural systems in the primate cerebral cortex. Proceedings, Biological Sciences, 252, 13–18.CrossRefGoogle Scholar
  163. Young, M., Scannell, J., Burns, G., & Blakemore, C. (1994). Analysis of connectivity: neural systems in the cerebral cortex. Reviews of Neuroscience, 5, 227–250.CrossRefGoogle Scholar
  164. Zhang, S., Bodenreider, O., & Golbreich, C. (2006). Experience in reasoning with the foundational model of anatomy in owl dl. Pacif ic Symposium on Biocomputing, 2006, 200–211.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of AnatomyRostockGermany

Personalised recommendations