Towards Differential Connectomics with NeuroVIISAS

  • Sebastian Schwanke
  • Jörg Jenssen
  • Peter Eipert
  • Oliver Schmitt
Software Original Article


The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.


Connectome Differential connectomics Neuronal networks Multidimensional connectomes Visualization Graph analysis Rat Nervous system 



We would like to thank Heidi Schumann and Christian Tominski (Computer Graphics, Institute of Computer Science, University of Rostock) for their helpful advice on the manuscript.


  1. Alper, B., Bach, B., Riche, N.H., Isenberg, T., Fekete, J.-D. (2013). Weighted graph comparison techniques for brain connectivity analysis. In Proceeding CHI ’13 proceedings of the SIGCHI conference on human factors in computing systems (pp. 483–492).CrossRefGoogle Scholar
  2. Amico, E., Marinazzo, D., Di Perri, C., Heine, L., Annen, J., Martial, C., Dzemidzic, M., Kirsch, M., Bonhomme, V., Laureys, S. (2017). Mapping the functional connectome traits of levels of consciousness. Neuroimage, 148, 201–211.PubMedCrossRefGoogle Scholar
  3. Anderle, M., Roy, S., Lin, H., Becker, C., Joho, K. (2004). Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum. Bioinformatics, 20, 3575–3582.PubMedCrossRefGoogle Scholar
  4. Bailey, P., De Barenne, J.C.D., Garol, H.W., McCulloch, W.S. (1940). Sensory cortex of chimpanzee. Journal of Neurophysiology, 3, 469–485.CrossRefGoogle Scholar
  5. Bajic, D., Craig, M.M., Borsook, D., Becerra, L. (2016). Probing intrinsic Resting-State networks in the infant rat brain. Frontiers in Behavioral Neuroscience, 10, 192.PubMedPubMedCentralCrossRefGoogle Scholar
  6. Baker, S.T., Lubman, D.I., Yücel, M., Allen, N.B., Whittle, S., Fulcher, B.D., Zalesky, A., Fornito, A. (2015). Developmental changes in brain network hub connectivity in late adolescence. Journal of Neuroscience, 35(24), 9078–9087.PubMedCrossRefGoogle Scholar
  7. Bakker, R., Wachtler, T., Diesmann, M. (2012). Cocomac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics, 27(6), 30.Google Scholar
  8. Beul, S.F., Grant, S., Hilgetag, C.C. (2015). A predictive model of the cat cortical connectome based on cytoarchitecture and distance. Brain Structure and Function, 220(6), 3167–3184.PubMedCrossRefGoogle Scholar
  9. Bota, M., Dong, H.W., Swanson, L.W. (2005). Brain architecture management system. Neuroinformatics, 3(1), 15–48.PubMedCrossRefGoogle Scholar
  10. Bota, M., Sporns, O., Swanson, L.W. (2015). Architecture of the cerebral cortical association connectome underlying cognition. Proceedings of the National Academy of Sciences of the United States of America, 112(16), E2093–E2101.PubMedPubMedCentralCrossRefGoogle Scholar
  11. Brandes, U., & Erlebach, T. (2005). Network analysis. Methodological foundations. LNCS 3418. Berlin: Springer.Google Scholar
  12. Brynildsen, J.K., Hsu, L.M., Ross, T.J., Stein, E.A., Yang, Y., Lu, H. (2017). Physiological characterization of a robust survival rodent fMRI method. Magnetic Resonance Imaging, 35, 54–60.PubMedCrossRefGoogle Scholar
  13. Caeyenberghs, K., & Leemans, A. (2014). Hemispheric lateralization of topological organization in structural brain networks. Human Brain Mapping, 35(9), 4944–4957.PubMedCrossRefGoogle Scholar
  14. Callaway, E.M., & Luo, L. (2015). Monosynaptic circuit tracing with Glycoprotein-Deleted rabies viruses. Journal of Neuroscience, 35(24), 8979–8985.PubMedCrossRefGoogle Scholar
  15. Cao, M., Shu, N., Cao, Q., Wang, Y., He, Y. (2014). Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder. Molecular Neurobiology, 50(3), 1111–1123.PubMedCrossRefGoogle Scholar
  16. Chung, K., Wallace, J., Kim, S.Y., Kalyanasundaram, S., Andalman, A.S., Davidson, T.J., Mirzabekov, J.J., Zalocusky, K.A., Mattis, J., Denisin, A.K., Pak, S., Bernstein, H., Ramakrishnan, C., Grosenick, L., Gradinaru, V., Deisseroth, K. (2013). Structural and molecular interrogation of intact biological systems. Nature, 497(7449), 332–337.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Collin, G., & van den Heuvel, M.P. (2013). The ontogeny of the human connectome: development and dynamic changes of brain connectivity across the life span. The Neuroscientist, 19(6), 616–628.PubMedCrossRefGoogle Scholar
  18. Crossley, N.A., Fox, P.T., Bullmore, E.T. (2016). Meta-connectomics: human brain network and connectivity meta-analyses. Psychological Medicine, 46(5), 897–907.PubMedCrossRefGoogle Scholar
  19. Dai, Z., Yan, C., Li, K., Wang, Z., Wang, J., Cao, M., Lin, Q., Shu, N., Xia, M., Bi, Y., He, Y. (2015). Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cerebral Cortex, 25(10), 3723–3742.PubMedCrossRefGoogle Scholar
  20. Daianu, M., Jacobs, R.E., Weitz, T.M., Town, T.C., Thompson, P.M. (2015). Multi-shell hybrid diffusion imaging (HYDI) at 7 Tesla in tgf344-AD transgenic Alzheimer rats. PLoS One, 10(12), e0145205.PubMedPubMedCentralCrossRefGoogle Scholar
  21. de Reus, M.A., & van den Heuvel, M.P. (2013). Rich club organization and intermodule communication in the cat connectome. Journal of Neuroscience, 33(32), 12929–12939.PubMedCrossRefGoogle Scholar
  22. Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P.C. (2012). Expanding the frontiers of visual analytics. New York: Springer.CrossRefGoogle Scholar
  23. Ding, S.L., Royall, J.J., Sunkin, S.M., Ng, L., Facer, B.A., Lesnar, P., Guillozet-Bongaarts, A., McMurray, B., Szafer, A., Dolbeare, T.A., Stevens, A., Tirrell, L., Benner, T., Caldejon, S., Dalley, R.A., Dee, N., Lau, C., Nyhus, J., Reding, M., Riley, Z.L., Sandman, D., Shen, E., van der Kouwe, A., Varjabedian, A., Write, M., Zollei, L., Dang, C., Knowles, J.A., Koch, C., Phillips, J.W., Sestan, N., Wohnoutka, P., Zielke, H.R., Hohmann, J.G., Jones, A.R., Bernard, A., Hawrylycz, M.J., Hof, P.R., Fischl, B., Lein, E.S. (2016). Comprehensive cellular-resolution atlas of the adult human brain. Journal of Comparative Neurology, 524(16), 3127–3481.PubMedCrossRefGoogle Scholar
  24. Epp, J.R., Niibori, Y., Liz Hsiang, H.L., Mercaldo, V., Deisseroth, K., Josselyn, S.A., Frankland, P.W. (2015). Optimization of CLARITY for clearing whole-brain and other intact organs. eNeuro 2(3), ENEURO.0022-15.2015.Google Scholar
  25. Felleman, D.J., & Van Essen, D.C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47.PubMedCrossRefGoogle Scholar
  26. Fornito, A., Zalesky, A., Pantelis, C., Bullmore, E.T. (2012). Schizophrenia, neuroimaging and connectomics. NeuroImage, 62(4), 2296–2314.PubMedCrossRefGoogle Scholar
  27. French, L., Liu, P., Marais, O., Koreman, T., Tseng, L., Lai, A., Pavlidis, P. (2015). Text mining for neuroanatomy using WhiteText with an upyeard corpus and a new web application. Frontiers in Neuroinformatics, 9, 13.PubMedPubMedCentralCrossRefGoogle Scholar
  28. García-Alcalde, F., García-López, F., Dopazo, J., Conesa, A. (2011). Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics, 27, 137–139.PubMedCrossRefGoogle Scholar
  29. Gerfen, C.R., & Sawchenko, P.E. (2016). An anterograde neuroanatomical tracing method that shows the detailed morphology of neurons, their axons and terminals: Immunohistochemical localization of an axonally transported plant lectin, Phaseolus vulgaris-leucoagglutinin (PHA-l). Brain Research, 1645, 42–45.PubMedCrossRefGoogle Scholar
  30. Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C. (2011). Visual comparison for information visualization. Information Visualization, 10(4), 289–309.CrossRefGoogle Scholar
  31. Gökdeniz, E., Özgür, A., Canbeyli, R. (2016). Automated neuroanatomical relation extraction: a linguistically motivated approach with a PVT connectivity graph case study. Frontiers in Neuroinformatics, 10, 39.PubMedPubMedCentralCrossRefGoogle Scholar
  32. Gong, Q., & He, Y. (2015). Depression, neuroimaging and connectomics: a selective overview. Biological Psychiatry, 77(3), 223–235.PubMedCrossRefGoogle Scholar
  33. Gutman, D.A., Keifer, O.P., Magnuson, M.E., Choi, D.C., Majeed, W., Keilholz, S., Ressler, K.J. (2012). A DTI tractography analysis of infralimbic and prelimbic connectivity in the mouse using high-throughput MRI. NeuroImage, 63(2), 800–811.PubMedPubMedCentralCrossRefGoogle Scholar
  34. Hannawi, Y., & Stevens, R.D. (2016). Mapping the connectome following traumatic brain injury. Current Neurology and Neuroscience Reports, 16(5), 44.PubMedCrossRefGoogle Scholar
  35. Harris, N.G., Verley, D.R., Gutman, B.A., Thompson, P.M., Yeh, H.J., Brown, J.A. (2016). Disconnection and hyper-connectivity underlie reorganization after TBI: a rodent functional connectomic analysis. Experimental Neurology, 277, 124–138.PubMedCrossRefGoogle Scholar
  36. Heilingoetter, C.L., & Jensen, M.B. (2016). Histological methods for ex vivo axon tracing: a systematic review. Neurological Research, 38(7), 561–569.PubMedPubMedCentralCrossRefGoogle Scholar
  37. Helmstädter, M. (2013). Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nature Methods, 10(6), 501–507.CrossRefGoogle Scholar
  38. Helmstädter, M., Briggman, K.L., Turaga, S.C., Jain, V., Seung, H.S., Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168–174.CrossRefGoogle Scholar
  39. Hendricksen, R. (2015). Visualizing differences between brain networks. Eindhoven University of Technology, Department of Mathematics and Computer Science. Eindhoven, M.Sc. thesis.Google Scholar
  40. Henriksen, S., Pang, R., Wronkiewicz, M. (2016). A simple generative model of the mouse mesoscale connectome. Elife, 5, e12366.PubMedPubMedCentralCrossRefGoogle Scholar
  41. Herdin, M., Czink, N., Özcelik, H., Bonek, H. (2005). Correlation matrix distance a meaningful measure for evaluation of non-stationary MIMO channels. In IEEE Xplore conference vehicular technology conference (Vol. 1, pp. 136–140).Google Scholar
  42. Hilgetag, C.C., Burns, G.A., O’Neill, M.A., Scannell, J.W., Young, M.P. (2000). 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(1393), 91–110.PubMedCrossRefGoogle Scholar
  43. Jirsa, V.K., & McIntosh, A.R. (2007). Handbook of brain connectivity. Berlin: Springer.CrossRefGoogle Scholar
  44. Johnson, G.A., Badea, A., Brandenburg, J., Cofer, G., Fubara, B., Liu, S., Nissanov, J. (2010). Waxholm space: an image-based reference for coordinating mouse brain research. NeuroImage, 53(2), 365–372.PubMedPubMedCentralCrossRefGoogle Scholar
  45. Kebschull, M., Fittler, M.J., Demmer, R.T., Papapanou, P.N. (2017). Differential expression and functional analysis of high-throughput -omics data using open source tools. Methods in Molecular Biology, 1537, 327–345.PubMedCrossRefGoogle Scholar
  46. Keifer, O.P., Gutman, D.A., Hecht, E.E., Keilholz, S.D., Ressler, K.J. (2015). A comparative analysis of mouse and human medial geniculate nucleus connectivity: a DTI and anterograde tracing study. NeuroImage, 105, 53–66.PubMedCrossRefGoogle Scholar
  47. Kennedy, H., Van Essen, D.C., Christen, Y. (2016). Micro- Meso- and Macro-connectomics of the brain. Berlin: Springer.CrossRefGoogle Scholar
  48. Kobeissy, F.H., Guingab-Cagmat, J.D., Zhang, Z., Moghieb, A., Glushakova, O.Y., Mondello, S., Boutté, A. M., Anagli, J., Rubenstein, R., Bahmad, H., Wagner, A.K., Hayes, R.L., Wang, K.K. (2016). Neuroproteomics and systems biology approach to identify temporal biomarker changes post experimental traumatic brain injury in rats. Frontiers in Neurology, 7, 198.PubMedPubMedCentralCrossRefGoogle Scholar
  49. Koelbl, C., Helmstädter, M., Lübke, J., Feldmeyer, D. (2015). A barrel-related interneuron in layer 4 of rat somatosensory cortex with a high intrabarrel connectivity. Cerebral Cortex, 25(3), 713–725.PubMedCrossRefGoogle Scholar
  50. Kuan, L., Li, Y., Lau, C., Feng, D., Bernard, A., Sunkin, S.M., Zeng, H., Dang, C., Hawrylycz, M., Ng, L. (2015). Neuroinformatics of the allen mouse brain connectivity atlas. Methods, 73, 4–17.PubMedCrossRefGoogle Scholar
  51. Kuo, T.C., Tian, T.F., Tseng, Y.J. (2013). 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Systems Biology, 7, 64.PubMedPubMedCentralCrossRefGoogle Scholar
  52. Lawhorn, C.M., Schomaker, R., Rowell, J.T., Rueppell, O. (2018). Simple comparative analyses of differentially expressed gene lists may overestimate gene overlap. Journal of Computational Biology, 25(6), 606–612.PubMedCrossRefGoogle Scholar
  53. Lee, T.H., Miernicki, M.E., Telzer, E.H. (2017). Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads. NeuroImage, 152, 31–37.PubMedPubMedCentralCrossRefGoogle Scholar
  54. Leonardi, N., Richiardi, J., Gschwind, M., Simioni, S., Annoni, J.M., Schluep, M., Vuilleumier, P., Van De Ville, D. (2013). Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage, 83, 937– 950.PubMedCrossRefGoogle Scholar
  55. Liang, X., Hsu, L.M., Lu, H., Sumiyoshi, A., He, Y., Yang, Y. (2018). The Rich-Club Organization in rat functional brain network to balance between communication cost and efficiency. Cerebral Cortex, 28(3), 924–935.PubMedGoogle Scholar
  56. Liu, Y.Y., Slotine, J.J., Barabási, A.L. (2011). Controllability of complex networks. Nature, 473(7346), 167–173.PubMedCrossRefGoogle Scholar
  57. Livet, J., Weissman, T.A., Kang, H., Draft, R.W., Lu, J., Bennis, R.A., Sanes, J.R., Lichtman, J.W. (2007). Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature, 450(7166), 56–62.PubMedCrossRefGoogle Scholar
  58. Ma, Y., Hamilton, C., Zhang, N. (2017). Dynamic connectivity patterns in conscious and unconscious brain. Brain Connect, 7(1), 1–12.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Mesulam, M.-M. (1982). Tracing neural connections with horseradish peroxidase. New York: Wiley.Google Scholar
  60. Newman, M.E.J. (2010). Networks. Oxford: Oxford University Press.CrossRefGoogle Scholar
  61. Oberländer, M., de Kock, C.P., Bruno, R.M., Ramirez, A., Meyer, H.S., Dercksen, V.J., Helmstädter, M., Sakmann, B. (2012). Cell type-specific three-dimensional structure of thalamocortical circuits in a column of rat vibrissal cortex. Cerebral Cortex, 22(10), 2375–2391.CrossRefGoogle Scholar
  62. Oh, S.W., Harris, J.A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A.M., Mortrud, M.T., Ouellette, B., Nguyen, T.N., Sorensen, S.A., Slaughterbeck, C.R., Wakeman, W., Li, Y., Feng, D., Ho, A., Nicholas, E., Hirokawa, K.E., Bohn, P., Joines, K.M., Peng, H., Hawrylycz, M.J., Phillips, J.W., Hohmann, J.G., Wohnoutka, P., Gerfen, C.R., Koch, C., Bernard, A., Dang, C., Jones, A.R., Zeng, H. (2014). A mesoscale connectome of the mouse brain. Nature, 508(7495), 207–214.PubMedPubMedCentralCrossRefGoogle Scholar
  63. Paasonen, J., Salo, R.A., Huttunen, J.K., Gröhn, O. (2016). Resting-state functional MRI as a tool for evaluating brain hemodynamic responsiveness to external stimuli in rats. Magnetic Resonance in Medicine, 78 (3), 1136–1146.PubMedCrossRefGoogle Scholar
  64. Papp, E.A., Leergaard, T.B., Calabrese, E., Johnson, G.A., Bjaalie, J.G. (2014). Waxholm Space atlas of the Sprague Dawley rat brain. NeuroImage, 97, 374–386.PubMedPubMedCentralCrossRefGoogle Scholar
  65. Parr-Brownlie, L.C., Bosch-Bouju, C., Schoderboeck, L., Sizemore, R.J., Abraham, W.C., Hughes, S.M. (2015). Lentiviral vectors as tools to understand central nervous system biology in mammalian model organisms. Frontiers in Molecular Neuroscience, 8, 14.PubMedPubMedCentralCrossRefGoogle Scholar
  66. Paxinos, G, & Watson, C. (2014). The rat brain in stereotaxic coordinates. 7 Aufl. San Diego: Academic Press.Google Scholar
  67. Paxinos, G., Watson, C., Calabrese, E., Badea, A., Johnson, G.A. (2015). MRI/DTI Atlas of the rat brain. San Diego: Academic Press.Google Scholar
  68. Preti, M.G., Bolton, T.A., Van De Ville, D. (2016). The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage, S1053-8119(16), 30788–1.Google Scholar
  69. Prettejohn, B.J., Berryman, M.J., McDonnell, M.D. (2011). Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists. Frontiers in Computational Neuroscience, 5, 11.PubMedPubMedCentralCrossRefGoogle Scholar
  70. Richardet, R., Chappelier, J.C., Telefont, M., Hill, S. (2015). Large-scale extraction of brain connectivity from the neuroscientific literature. Bioinformatics, 31(10), 1640–1647.PubMedPubMedCentralCrossRefGoogle Scholar
  71. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52, 1059–1069.PubMedCrossRefGoogle Scholar
  72. Rumple, A., McMurray, M., Johns, J., Lauder, J., Makam, P., Radcliffe, M., Oguz, I. (2013). 3-dimensional diffusion tensor imaging (DTI) atlas of the rat brain. PLoS One, 8(7), e67334.PubMedPubMedCentralCrossRefGoogle Scholar
  73. Scannell, J.W., & Young, M.P. (1993). The connectional organization of neural systems in the cat cerebral cortex. Current Biology, 3(4), 191–200.PubMedCrossRefGoogle Scholar
  74. Scannell, J.W., Blakemore, C., Young, M.P. (1995). Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience, 15(2), 1463–1483.PubMedCrossRefGoogle Scholar
  75. Scannell, J.W., Burns, G.A., Hilgetag, C.C., O’Neil, M.A., Young, M.P. (1999). The connectional organization of the cortico-thalamic system of the cat. Cerebral Cortex, 9(3), 277–299.PubMedCrossRefGoogle Scholar
  76. Schmitt, O., & Eipert, P. (2012). NeuroVIISAS: approaching multiscale simulation of the rat connectome. Neuroinformatics, 10(3), 243–267.PubMedCrossRefGoogle Scholar
  77. Schmitt, O., Eipert, P., Philipp, K., Kettlitz, R., Füllen, G., Wree, A. (2012). The intrinsic connectome of the rat amygdala. Front Neural Circuits, 6, 81.PubMedPubMedCentralCrossRefGoogle Scholar
  78. Schmitt, O., Eipert, P., Kettlitz, R., Lemann, F., Wree, A. (2016). The connectome of the basal ganglia. Brain Structure and Function, 221(2), 753–814.PubMedCrossRefGoogle Scholar
  79. Schmitt, O., Badurek, S., Liu, W., Wang, Y., Rabiller, G., Kanoke, A., Eipert, P., Liu, J. (2017). Prediction of regional functional impairment following experimental stroke via connectome analysis. Science Reports, 7, 46316.CrossRefGoogle Scholar
  80. Shen, X., Finn, E.S., Scheinost, D., Rosenberg, M.D., Chun, M.M., Papademetris, X., Constable, R.T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3), 506–518.PubMedPubMedCentralCrossRefGoogle Scholar
  81. Shipp, S. (2005). The importance of being agranular: a comparative account of visual and motor cortex. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences, 360(1456), 797–814.PubMedCrossRefGoogle Scholar
  82. Simoff, S.J., Böhlen, M.H., Mazeika, A. (2008). Visual data mining. Theory, techniques and tools for visual analytics. Lecture notes in computer science 4404. London: Springer.Google Scholar
  83. Sizemore, R.J., Seeger-Armbruster, S., Hughes, S.M., Parr-Brownlie, L.C. (2016). Viral vector-based tools advance knowledge of basal ganglia anatomy and physiology. Journal of Neurophysiology, 115(4), 2124–2146.PubMedPubMedCentralCrossRefGoogle Scholar
  84. Smith, J.B., Liang, Z., Watson, G.D., Alloway, K.D., Zhang, N. (2016). Interhemispheric resting-state functional connectivity of the claustrum in the awake and anesthetized states. Brain Structure and Function, 222(5), 2041–2058.PubMedCrossRefGoogle Scholar
  85. Sporns, O. (2011). Networks of the brain. Cambridge: The MIT Press.Google Scholar
  86. Sporns, O. (2012). Discovering the human connectome. Cambridge: The MIT Press.Google Scholar
  87. Stephan, K.E., Zilles, K., Kötter, R. (2000). Coordinate-independent mapping of structural and functional data by objective relational transformation (ORT). Philosophical Transactions of the Royal Society of London Series B: Biological Sciences, 355(1393), 37–54.PubMedCrossRefGoogle Scholar
  88. Stephan, K.E., Kamper, L., Bozkurt, A., Burns, G.A., Young, M.P., 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(1412), 1159–1186.PubMedCrossRefGoogle Scholar
  89. Sugar, J., Witter, M.P., van Strien, N.M., Cappaert, N.L.M. (2011). The retrosplenial cortex: intrinsic connectivity and connections with the (para)hippocampal region in the rat. An interactive connectome. Frontiers in Neuroinformatics, 5, 7.PubMedPubMedCentralCrossRefGoogle Scholar
  90. Sukhinin, D.I., Engel, A.K., Manger, P., Hilgetag, C.C. (2016). Building the ferretome. Frontiers in Neuroinformatics, 10(10), 16.PubMedPubMedCentralGoogle Scholar
  91. Sun, Y., Lee, R., Chen, Y., Collinson, S., Thakor, N., Bezerianos, A., Sim, K. (2015). Progressive gender differences of structural brain networks in healthy adults: a longitudinal, diffusion tensor imaging study. PLoS One, 10(3), e0118857.PubMedPubMedCentralCrossRefGoogle Scholar
  92. Swanson, L.W. (2004). Brain maps: Structure of the rat brain, 3rd Edn. Amsterdam: Elsevier.Google Scholar
  93. Swanson, L.W. (2014). Neuroanatomical terminology. A lexicon of classical origins and historical foundations. Oxford: Oxford University Press.Google Scholar
  94. Swanson, L.W., & Bota, M. (2010). Foundational model of structural connectivity in the nervous system with a schema for wiring diagrams, connectome, and basic plan architecture. Proceedings of the National Academy of Sciences of the United States of America, 107(48), 20610–20617.PubMedPubMedCentralCrossRefGoogle Scholar
  95. Swanson, L.W., Sporns, O., Hahn, J.D. (2016). Network architecture of the cerebral nuclei (basal ganglia) association and commissural connectome. Proceedings of the National Academy of Sciences of the United States of America, 113(40), E5972–E5981.PubMedPubMedCentralCrossRefGoogle Scholar
  96. Symons, S., & Nieselt, K. (2011). MGV: a generic graph viewer for comparative omics data. Bioinformatics, 27, 2248–2255.PubMedCrossRefGoogle Scholar
  97. Tomer, R., Ye, L., Hsueh, B., Deisseroth, K. (2014). Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nature Protocols, 9(7), 1682–1697.PubMedPubMedCentralCrossRefGoogle Scholar
  98. Ugolini, G. (2011). Rabies virus as a transneuronal tracer of neuronal connections. Advances in Virus Research, 79, 165–202.PubMedCrossRefGoogle Scholar
  99. van den Heuvel, M.P., Sporns, O., Collin, G., Scheewe, T., Mandl, R.C., Cahn, W., Goñi, J., Hulshoff Pol, H.E., Kahn, R.S. (2012). Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry, 70(8), 783–792.CrossRefGoogle Scholar
  100. van den Heuvel, M.P., Scholtens, L.H., de Reus, M.A. (2016). Topological organization of connectivity strength in the rat connectome. Brain Structure and Function, 221, 1719– 1736.PubMedCrossRefGoogle Scholar
  101. Vasques, X., Richardet, R., Hill, S.L., Slater, D., Chappelier, J.C., Pralong, E., Bloch, J., Draganski, B., Cif, L. (2015). Automatic target validation based on neuroscientific literature mining for tractography. Frontiers in Neuroanatomy, 9, 66.PubMedPubMedCentralCrossRefGoogle Scholar
  102. Verbeeck, N., Spraggins, J.M., Murphy, M.J., Wang, H.D., Deutch, A.Y., Caprioli, R.M., de Plas, R.V. (2017). Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis. Biochimica et Biophysica Acta, S1570-9639(17), 30040–30047.Google Scholar
  103. Vértes, P.E., & Bullmore, E.T. (2015). Annual research review: Growth connectomics - the organization and reorganization of brain networks during normal and abnormal development. Journal of Child Psychology and Psychiatry, 56(3), 299–320.PubMedCrossRefGoogle Scholar
  104. Wanner, A.A., Kirschmann, M.A., Genoud, C. (2015). Challenges of microtome-based serial block-face scanning electron microscopy in neuroscience. Journal de Microscopie, 259(2), 137–142.CrossRefGoogle Scholar
  105. Wheeler, D.W., White, C.M., Rees, C.L., Komendantov, A.O., Hamilton, D.J., Ascoli, G.A. (2015). a knowledge base of neuron types in the rodent hippocampus. Elife, 4, e09960.PubMedPubMedCentralGoogle Scholar
  106. White, J.G., Southgate, E., Thomson, J.N., Brenner, S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences, 314(1165), 1–340.PubMedCrossRefGoogle Scholar
  107. Wille, M., Schümann, A., Wree, A., Kreutzer, M., Glocker, M.O., Mutzbauer, G., Schmitt, O. (2015a). The proteome profiles of the cerebellum of juvenile, adult and aged rats - an ontogenetic study. International Journal of Molecular Sciences, 16(9), 21454– 21485.PubMedPubMedCentralCrossRefGoogle Scholar
  108. Wille, M., Schümann, A., Kreutzer, M., Glocker, M.O., Wree, A., Mutzbauer, G., Schmitt, O. (2015b). The proteome profiles of the olfactory bulb of juvenile, adult and aged rats - an ontogenetic study. Proteome Science, 15(13), 8.CrossRefGoogle Scholar
  109. Wille, M., Schümann, A., Kreutzer, M., Glocker, M.O., Wree, A., Mutzbauer, G., Schmitt, O. (2017). Differential proteomics of the cerebral cortex of juvenile, adult and aged rats - an ontogenetic study. Journal of Proteomics and Bioinformatics, in press.Google Scholar
  110. Wouterlood, F.G., Bloem, B., Mansvelder, H.D., Luchicchi, A., Deisseroth, K. (2014). A fourth generation of neuroanatomical tracing techniques: exploiting the offspring of genetic engineering. Journal of Neuroscience Methods, 235, 331–348.PubMedCrossRefGoogle Scholar
  111. Xia, M., & He, Y. (2017). Functional connectomics from a “big data” perspective. Neuroimage, S1053-8119 (17), 30142–8.Google Scholar
  112. Yau, N. (2013). Data points: visualizing that means something. Indianapolis: Wiley.Google Scholar
  113. Young, M.P. (1992). Objective analysis of the topological organization of the primate cortical visual system. Nature, 358(6382), 152–155.PubMedCrossRefGoogle Scholar
  114. Young, M.P., Scannell, J.W., Burns, G.A., Blakemore, C. (1994). Analysis of connectivity: neural systems in the cerebral cortex. Reviews in the Neurosciences, 5(3), 227–250.PubMedCrossRefGoogle Scholar
  115. Zaborszky, L., Wouterlood, F.G., Lancietgo, J.L. (2006). Neuroanatomical tract-tracing 3. Molecules, neurons and systems. Singerpore: Springer.CrossRefGoogle Scholar
  116. Zador, A.M., Dubnau, J., Oyibo, H.K., Zhan, H., Cao, G., Peikon, I.D. (2012). Sequencing the connectome. PLoS Biology, 10(10), e1001411.PubMedPubMedCentralCrossRefGoogle Scholar
  117. Zaslavsky, I., Baldock, R.A., Boline, J. (2014). Cyberinfrastructure for the digital brain: spatial standards for integrating rodent brain atlases. Frontiers in Neuroinformatics, 8, 74.PubMedPubMedCentralCrossRefGoogle Scholar
  118. Zeng, T., Chen, H., Fakhry, A., Hu, X., Liu, T., Ji, S. (2015). Allen mouse brain atlases reveal different neural connection and gene expression patterns in cerebellum gyri and sulci. Brain Structure and Function, 220(5), 2691–703.PubMedCrossRefGoogle Scholar
  119. Zuo, X.N., He, Y., Betzel, R.F., Colcombe, S., Sporns, O., Milham, M.P. (2017). Human connectomics across the life span. Trends in Cognitive Sciences, 21(1), 32–45.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of AnatomyUniversity of RostockRostockGermany

Personalised recommendations