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2014 | OriginalPaper | Buchkapitel

21. Visualization in Connectomics

verfasst von : Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege, Jos B. T. M. Roerdink

Erschienen in: Scientific Visualization

Verlag: Springer London

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Abstract

Connectomics is a branch of neuroscience that attempts to create a connectome, i.e., a complete map of the neuronal system and all connections between neuronal structures. This representation can be used to understand how functional brain states emerge from their underlying anatomical structures and how dysfunction and neuronal diseases arise. We review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe a number of challenges. After a brief summary of the biological background and an overview of relevant imaging modalities, we review current techniques to extract connectivity information from image data at macro-, meso- and microscales. We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks.

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Vorheriges Kapitel Overview of Visualization in Biology and Medicine

Nächstes Kapitel Visualization in Biology and Medicine

http://flybrain.neurobio.arizona.edu

Similar approaches have been used in genomics [62, 84] and other areas.

Appel, N.M.: Classical and contemporary histochemical approaches for evaluating central nervous system microanatomy. Ann. NY Acad. Sci. 820(1), 14–28 (1997)CrossRef

Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44(4), 625–632 (2000)CrossRef

Bassett, D.S., Bullmore, E.: Small-world brain networks. Neuroscientist 12(6), 512–523 (2006)CrossRef

Behrens, T.E., Woolrich, M.W., Jenkinson, M., JohansenBerg, H., Nunes, R.G., Clare, S., Matthews, P.M., Brady, J.M., Smith, S.M.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50(5), 1077–1088 (2003)CrossRef

Bertrand, L., Nissanov, J.: The neuroterrain 3D mouse brain atlas. Front. Neuroinform. 2, 3 (2008)CrossRef

Bezgin, G., Reid, A., Schubert, D., Kötter, R.: Matching spatial with ontological brain regions using java tools for visualization, database access, and integrated data analysis. Neuroinformatics 7(1), 7–22 (2009)CrossRef

Bjaalie, J.G.: Localization in the brain: new solutions emerging. Nat. Rev. Neurosci. 3, 322–325 (2002)CrossRef

Blaas, J., Botha, C.P., Peters, B., Vos, F.M., Post, F.H.: Fast and reproducible fiber bundle selection in DTI visualization. In: Silva, C., Gröller, E., Rushmeier, H. (eds.) Proceedings of IEEE Visualization 2005, pp. 59–64 (2005)

Bruckner, S., Gröller, M.: Instant volume visualization using maximum intensity difference accumulation. Comput. Graph. Forum 28(3), 775–782 (2009)CrossRef

10.

Bullmore, E., Harrison, L., Lee, L., Mechelli, A., Friston, K.(eds.): Special issue on functional connectivity. Neuroinformatics 2(2) (2004)

11.

Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009). see also Corrigendum (March 3, 2009)

12.

Bullmore, E., Bassett, D.: Brain graphs: graphical models of the human brain connectome. Ann. Rev. Clin. Psychol. 7, 113–140 (2011)CrossRef

13.

Burns, G.a.P.C., Cheng, W.C., Thompson, R.H., Swanson, L.W.: The NeuARt II system: A viewing tool for neuroanatomical data based on published neuroanatomical atlases. BMC Bioinform. 7, 531–549 (2006)

14.

Cardona, A., Saalfeld, S., Preibisch, S., Schmid, B., Cheng, A., Pulokas, J., Tomancak, P., Hartenstein, V.: An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol 8(10), e1000,502 (2010)

15.

Chicurel, M.: Databasing the brain. Nature 406(6798), 822–825 (2000)CrossRef

16.

Chklovskii, D.B., Vitaladevuni, S., Scheffer, L.K.: Semi-automated reconstruction of neural circuits using electron microscopy. Curr. Opin. Neurobiol. 20(5), 667–675 (2010)CrossRef

17.

Conchello, J.A., Lichtman, J.: Optical sectioning microscopy. Nat. Methods 2(12), 920–931 (2005)CrossRef

18.

Crippa, A., Roerdink, J.B.T.M.: Data-driven visualization of functional brain regions from resting state fMRI data. In: Eisert, P., Polthier, K., Hornegger, J. (eds.) Proceedings Vision, Modeling and Visualization Workshop (VMV), 4–6 Oct, Berlin, pp. 247–254 (2011)

19.

Crippa, A., Maurits, N.M., Roerdink, J.B.T.M.: Graph averaging as a means to compare multichannel EEG coherence networks and its application to the study of mental fatigue and neurodegenerative disease. Comput. Graph. 35(2), 265–274 (2011)CrossRef

20.

Da Costa, N.M., Fürsinger, D., Martin, K.A.C.: The synaptic organization of the claustral projection to the cat’s visual cortex. J. Neurosci. 30(39), 13,166–13,170 (2010)

21.

Denk, W., Horstmann, H.: Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2(11), e329 (2004)CrossRef

22.

Dercksen, V.J., Oberlaender, M., Sakmann, B., Hege, H.C.: Interactive Visualization—a Key Prerequisite for Reconstruction of Anatomically Realistic Neural Networks. In: Linsen, L. (ed.) Visualization in Medicine and Life Sciences II. Springer, Berlin (2012)

23.

Einstein, A.: Investigations on the Theory of the Brownian Movement. Dover (1956)

24.

Emmenlauer, M., Ronneberger, O., Ponti, A., Schwarb, P., Griffa, A., Filippi, A., Nitschke, R., Driever, W., Burkhardt, H.: XuvTools: free, fast and reliable stitching of large 3D datasets. J. Microsc. 233(1), 42–60 (2009)MathSciNetCrossRef

25.

Enders, F., Sauber, N., Merhof, D., Hastreiter, P., Nimsky, C., Stamminger, M.: Visualization of white matter tracts with wrapped streamlines. In: IEEE Visualization, 2005. VIS 05, pp. 51–58. IEEE (2005)

26.

Everts, M.H., Bekker, H., Roerdink, J.B.T.M., Isenberg, T.: Depth-dependent halos: illustrative rendering of dense line data. IEEE Trans. Vis. Comput. Graph. 15(6), 1299–1306 (2009)CrossRef

27.

Fair, D.A., Cohen, A.L., Power, J.D., Dosenbach, N.U.F., Church, J.A., Miezin, F.M., Schlaggar, B.L., Petersen, S.E.: Functional brain networks develop from a “local to distributed” organization. PLoS Comput. Biol. 5(5), e1000,381 (2009)

28.

Fair, D.A., Cohen, A.L., Dosenbach, N.U.F., Church, J.A., Miezin, F.M., Barch, D.M., Raichle, M.E., Petersen, S.E., Schlaggar, B.L.: The maturing architecture of the brain’s default network. Proc. Natl. Acad. Sci. 105(10), 4028–4032 (2008)CrossRef

29.

Fiala, J.C.: Reconstruct: a free editor for serial section microscopy. J. Microsc. 218(1), 52–61 (2005)MathSciNetCrossRef

30.

Fornito, A., Zalesky, A., Pantelis, C., Bullmore, E.T.: Schizophrenia, neuroimaging and connectomics. NeuroImage 62, 2296–2314 (2012)

31.

Fredriksson, J.: Design of an Internet accessible visual human brain database system. In: Proceedings of IEEE International Conference on Multimedia Computing and Systems, vol. 1, pp. 469–474 (1999)

32.

Friston, K.J.: Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56–78 (1994)CrossRef

33.

Gan, W.B., Grutzendler, J., Wong, W.T., Wong, R.O.L., Lichtman, J.W.: Multicolor “DiOlistic” Labeling Neurotechnique of the Nervous System Using. Neuron 27, 219–225 (2000)CrossRef

34.

Hagmann, P., Jonasson, L., Maeder, P., Thiran, J., Wedeen, V.J., Meuli, R.: Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 26(Suppl 1), S205–S223 (2006)CrossRef

35.

Hämäläinen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J., Lounasmaa, O.V.: Magneto-encephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65, 413–497 (1993)CrossRef

36.

Hayworth, K.J., Kasthuri, N., Schalek, R., Lichtman, J.W.: Automating the collection of ultrathin serial sections for large volume TEM reconstructions. Microsc. Microanal. 12(S02), 86–87 (2006)CrossRef

37.

Helmstaedter, M., Briggman, K.L., Denk, W.: High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 14, 1081–1088 (2011)CrossRef

38.

Huberman, A.D., Niell, C.M.: What can mice tell us about how vision works? Trends Neurosci. 34(9), 464–473 (2011)CrossRef

39.

Jain, V., Bollmann, B., Richardson, M., Berger, D.R., Helmstaedter, M.N., Briggman, K.L., Denk, W., Bowden, J.B., Mendenhall, J.M., Abraham, W.C., Harris, K.M., Kasthuri, N., Hayworth, K.J., Schalek, R., Tapia, J.C., Lichtman, J.W., Seung, H.S.: Boundary learning by optimization with topological constraints. IEEE Conference on Computer Vision and Pattern Recognition pp. 2488–2495 (2010)

40.

Jenett, A., Schindelin, J.E., Heisenberg, M.: The virtual insect brain protocol: creating and comparing standardized neuroanatomy. BMC Bioinform. 7(1), 544–555 (2006)CrossRef

41.

Jeong, W.K., Johnson, M.K.: Display-aware Image Editing. IEEE International Conference on Computational Photography (2011)

42.

Jeong, W.K., Beyer, J., Hadwiger, M., Blue, R., Law, C., Vazquez-Reina, A., Reid, R.C., Lichtman, J., Pfister, H.: Ssecrett and neurotrace: interactive visualization and analysis tools for large-scale neuroscience data sets. IEEE Comput. Graph. Appl. 30(3), 58–70 (2010)CrossRef

43.

Joshi, S.H., Horn, J.D.V., Toga, A.W.: Interactive exploration of neuroanatomical meta-spaces. Front. Neuroinform. 3, 38 (2009)CrossRef

44.

Jurrus, E., Paiva, A.R.C., Watanabe, S., Anderson, J.R., Jones, B.W., Whitaker, R.T., Jorgensen, E.M., Marc, R.E., Tasdizen, T.: Detection of neuron membranes in electron microscopy images using a serial neural network architecture. Med. Image Anal. 14(6), 770–783 (2010)CrossRef

45.

Kaiser, M.: A tutorial in connectome analysis: Topological and spatial features of brain networks. NeuroImage 57(3), 892–907 (2011) (Special Issue: Educational Neuroscience)

46.

Kaynig, V., Fischer, B., Buhmann, J.M.: Probabilistic image registration and anomaly detection by nonlinear warping. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

47.

Kaynig, V., Fuchs, T., Buhmann, J.M.: Geometrical consistent 3D tracing of neuronal processes in ssTEM data. In: International Conference on Medical Image Computing and Computer Assisted Intervention (2010)

48.

Kaynig, V., Fuchs, T., Buhmann, J.M.: Neuron geometry extraction by perceptual grouping in ssTEM images. In: T. Fuchs, J.M. Buhmann (eds.) IEEE Conference on Computer Vision and Pattern Recognition, pp. 2902–2909. IEEE (2010)

49.

Kindlmann, G., Weinstein, D.: Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields. In: Proceedings of Visualization ’99, pp. 183–524. IEEE (1999)

50.

Kindlmann, G.: Superquadric tensor glyphs. In: Proceedings of IEEE TVCG/EG Symposium on Visualization, pp. 147–154 (2004)

51.

Klein, J., Friman, O., Hadwiger, M., Preim, B., Ritter, F., Vilanova, A., Zachmann, G., Bartz, D.: Visual computing for medical diagnosis and treatment. Comput. Graph. 33(4), 554–565 (2009)CrossRef

52.

Knott, G., Marchman, H., Wall, D., Lich, B.: Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 28(12), 2959–2964 (2008)CrossRef

53.

Koslow, S.H., Subramaniam, S.: Databasing the Brain: From Data to Knowledge. Wiley (2005)

54.

Kötter, R.: Online retrieval, processing, and visualization of primate connectivity data from the cocomac database. Neuroinformatics 2(2), 127–44 (2004)CrossRef

55.

Kuß, A., Prohaska, S., Meyer, B., Rybak, J., Hege, H.C.: Ontology-based visualization of hierarchical neuroanatomical structures. In: Proc. Eurographics Workshop on Visual Computing for Biomedicine, VCBM, Delft, The Netherlands, pp. 177–184 (2008)

56.

Kuß, A., Gensel, M., Meyer, B., Dercksen, V., Prohaska, S.: Effective techniques to visualize filament-surface relationships. Comput. Graph. Forum 29(3), 1003–1012 (2010)CrossRef

57.

Lang, S., Dercksen, V.J., Sakmann, B., Oberlaender, M.: Simulation of signal flow in three-dimensional reconstructions of an anatomically realistic neuronal network in rat vibrissal cortex. Neural Netw. 24(9), 998–1011 (2011)CrossRef

58.

Lau, C., Ng, L., Thompson, C., Pathak, S., Kuan, L., Jones, A., Hawrylycz, M.: Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain. BMC Bioinform. 9, 153–163 (2008)CrossRef

59.

de Leeuw, W., Verschure, P.J., van Liere, R.: Visualization and analysis of large data collections: a case study applied to confocal microscopy data. IEEE Trans. Vis. Comput. Graph. 12(5), 1251–1258 (2006)CrossRef

60.

Lichtman, J., Livet, J., Sanes, J.: A technicolour approach to the connectome. Nat. Rev. Neurosci. 9(6), 417–422 (2008)CrossRef

61.

Lin, C.Y., Tsai, K.L., Wang, S.C., Hsieh, C.H., Chang, H.M., Chiang, A.S.: The Neuron Navigator: Exploring the information pathway through the neural maze. In: Proceedings of IEEE Pacific Visualization 2011, pp. 35–42. IEEE (2011)

62.

Luscombe, N.M., Babu, M.M., Yu, H., Snyder, M., Teichmann, S.A., Gerstein, M.: Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004)CrossRef

63.

Mackay, T.F., Anholt, R.R.: Of flies and man: drosophila as a model for human complex traits. Ann. Rev. Genomics Hum. Genet. 7, 339–367 (2006)CrossRef

64.

Maye, A., Wenckebach, T.H., Hege, H.C.: Visualization, reconstruction, and integration of neuronal structures in digital brain atlases. Int. J. Neurosci. 116(4), 431–459 (2006)CrossRef

65.

Oberlaender, M., de Kock, C.P.J., Bruno, R.M., Ramirez, A., Meyer, H.S., Dercksen, V.J., Helmstaedter, M., Sakmann, B.: Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex. Cerebral Cortex. 22(10), 2375–2395 (2012) doi:10.1093/cercor/bhr317

66.

Oberlaender, M., Dercksen, V.J., Egger, R., Gensel, M., Sakmann, B., Hege, H.C.: Automated three-dimensional detection and counting of neuron somata. J. Neurosci. Methods 180(1), 147–160 (2009)CrossRef

67.

Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W.: Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci. 87(24), 9868–9872 (1990)CrossRef

68.

Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.G., Merkle, H., Ugurbil, K.: Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. 89(13), 5951–5955 (1992)CrossRef

69.

Özarslan, E., Mareci, T.H.: Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging. Magn. Reson. Med. 50(5), 955–965 (2003)CrossRef

70.

Peeters, T.H., Prčkovska, V., van Almsick, M., Vilanova, A., ter Haar Romeny, B.M.: Fast and sleek glyph rendering for interactive HARDI data exploration. In: Visualization Symposium, 2009. PacificVis ’09. IEEE Pacific, pp. 153–160. IEEE (2009)

71.

Pereanu, W., Hartenstein, V.: Neural lineages of the drosophila brain: a three-dimensional digital atlas of the pattern of lineage location and projection at the late larval stage. J. Neurosci. 26(20), 5534–5553 (2006)CrossRef

72.

Perrin, M., Poupon, C., Cointepas, Y., Rieul, B., Golestani, N., Pallier, C., Rivire, D., Constantinesco, A., Bihan, D., Mangin, J.F.: Fiber tracking in q-Ball fields using regularized particle trajectories. In: Christensen, G.E., Sonka, M. (eds.) Inf. Process. Med. Imaging, vol. 3565, pp. 52–63. Springer, Berlin (2005)CrossRef

73.

Prčkovska, V., Peeters, T.H., van Almsick, M., ter, : Haar Romeny, B., Vilanova i Bartroli, A.: Fused DTI/HARDI visualization. IEEE Trans. Vis. Comput. Graph. 17(10), 1407–1419 (2011)

74.

Preibisch, S., Saalfeld, S., Tomancak, P.: Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25(11), 1463–1465 (2009)CrossRef

75.

Press, W.A., Olshausen, B.A., Essen, D.C.V.: A graphical anatomical database of neural connectivity. Philos. Trans. R. Soc. 356(1412), 1147–1157 (2001)CrossRef

76.

Reina, A.V., Miller, E., Pfister, H.: Multiphase geometric couplings for the segmentation of neural processes. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2009)

77.

Roberts, M., Jeong, W.K., V, A., Unger, M.: Neural Process Reconstruction from Sparse User Scribbles. In: Medical Image Computing and Computer Assisted Intervention, pp. 1–8 (2011)

78.

Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 52, 1059–1069 (2010)CrossRef

79.

Rybak, J., Kuss, A., Lamecker, H., Zachow, S., Hege, H., Lienhard, M., Singer, J., Neubert, K., Menzel, R.: The digital bee brain: integrating and managing neurons in a common 3d reference system. Frontiers in systems neuroscience 4 (2010)

80.

Salvador, R., Suckling, J., Coleman, M.R.: Pickard John, D., Menon, D., Bullmore, E.: Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 15, 1332–1342 (2005)

81.

Schomer, D.L., Lopes da Silva, F.: Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Wolters Kluwer/Lippincott Williams & Wilkins (2010)

82.

Schultz, T., Theisel, H., Seidel, H.P.: Topological visualization of brain diffusion MRI data. IEEE Trans. Vis. Comput. Graph. 13(6), 1496–1503 (2007)CrossRef

83.

Seung, S.: Connectome. Houghton Mifflin Harcourt (2011). In press

84.

Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006)CrossRef

85.

Sherbondy, A., Akers, D., Mackenzie, R., Dougherty, R., Wandell, B.: Exploring connectivity of the brain’s white matter with dynamic queries. IEEE Trans. Vis. Comput. Graph. 11(4), 419–430 (2005)CrossRef

86.

Shu, N., Liu, Y., Li, J., Li, Y., Yu, C., Jiang, T.: Altered anatomical network in early blindness revealed by diffusion tensor tractography. PLoS One 4(9), e7228 (2009)CrossRef

87.

Sporns, O.: Networks of the brain. MIT Press, Cambridge (2010)

88.

Sporns, O., Zwi, J.: The small world of the cerebral cortex. Neuroinformatics 2, 145–162 (2004)CrossRef

89.

Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)CrossRef

90.

Stam, C.J., Reijneveld, J.C.: Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed. Phys. 1 (2007)

91.

Straehle, C., Köthe, U., Knott, G., Hamprecht, F.: Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. In: MICCAI, pp. 657–664 (2011)

92.

Ten Caat, M., Maurits, N.M., Roerdink, J.B.T.M.: Functional unit maps for data-driven visualization of high-density EEG coherence. In: Proceedings of Eurographics/IEEE VGTC Symposium on Visualization (EuroVis), pp. 259–266 (2007)

93.

Ten Caat, M.: Multichannel EEG visualization. Ph.D. thesis, Institute of Mathematics and Computing Science, University of Groningen, The Netherlands (2008)

94.

Ten Caat, M., Maurits, N.M., Roerdink, J.B.T.M.: Data-driven visualization and group analysis of multichannel EEG coherence with functional units. IEEE Trans. Vis. Comput. Graph. 14(4), 756–771 (2008)CrossRef

95.

Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48(4), 577–582 (2002)CrossRef

96.

Tuch, D.S.: Qball imaging. Magn. Reson. Med. 52(6), 1358–1372 (2004)CrossRef

97.

Türetken, E., González, G., Blum, C., Fua, P.: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9(2), 279–302 (2011)CrossRef

98.

Valiant, L.G.: A quantitative theory of neural computation. Biol. Cybern. 95(3), 205–211 (2006)MATHCrossRef

99.

Van Dixhoorn, A., Vissers, B., Ferrarini, L., Milles, J., Botha, C.P.: Visual analysis of integrated resting state functional brain connectivity and anatomy. In: Proc. Eurographics Workshop on Visual Computing for Biomedicine, VCBM, Leipzig, Germany, pp. 57–64 (2010)

100.

Varshney, L.R., Chen, B.L., Paniagua, E., Hall, D.H., Chklovskii, D.B.: Structural properties of the caenorhabditis elegans neuronal network. PLoS Comput. Biol. 7(2), 21 (2011)

101.

Vazquez-Reina, A., Pfister, H., Miller, E.L.: Segmentation Fusion for Connectomics. International Conference on Computer Vision pp. 1–8 (2011)

102.

Vilanova, A., Zhang, S., Kindlmann, G., Laidlaw, D.H.: An introduction to visualization of diffusion tensor imaging and its applications. In: Visualization and Image Processing of Tensor Fields. Springer (2005)

103.

Vitaladevuni, S.N.: Co-clustering of image segments using convex optimization applied to em neuronal reconstruction. IEEE Conference on Computer Vision and Pattern Recognition pp. 2203–2210 (2010)

104.

Walter, T., Shattuck, D.W., Baldock, R., Bastin, M.E., Carpenter, A.E., Duce, S., Ellenberg, J., Fraser, A., Hamilton, N., Pieper, S.: Ragan, M.a., Schneider, J.E., Tomancak, P., Hériché, J.K.: Visualization of image data from cells to organisms. Nat. Methods 7(3s), S26–S41 (2010)

105.

Wan, Y., Otsuna, H., Chien, C.B., Hansen, C.: An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research. IEEE Trans. Vis. Comput. Graph. 15(6), 1489–1496 (2009)CrossRef

106.

White, J.G., Southgate, E., Thomson, J.N., Brenner, S.: The structure of the nervous system of the nematode caenorhabditis elegans. Philos. Trans. R. Soc. B Biol. Sci. 314(1165), 1–340 (1986)CrossRef

107.

Worsley, K.J., Chen, J., Lerch, J., Evans, A.C.: Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos. Trans. R. Soc. B Biol. Sci. 360(1457), 913–920 (2005)CrossRef

108.

Zalesky, A., Fornito, A., Bullmore, E.T.: Network-based statistic: identifying differences in brain networks. Neuroimage 53(4), 1197–1207 (2010)CrossRef

109.

Zhang, S., Demiralp, C., Laidlaw, D.H.: Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. IEEE Trans. Vis. Comput. Graph. 9(4), 454–462 (2003)CrossRef

Titel: Visualization in Connectomics
verfasst von: Hanspeter Pfister
Verena Kaynig
Charl P. Botha
Stefan Bruckner
Vincent J. Dercksen
Hans-Christian Hege
Jos B. T. M. Roerdink
Verlag: Springer London
Buch: Scientific Visualization
Print ISBN: 978-1-4471-6496-8

Electronic ISBN: 978-1-4471-6497-5

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-1-4471-6497-5_21

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