Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome

PLoS ONE

Volumn 9, Issue 7, 2014, Pages

  Pavlovic, Dragana M ^a   Vértes, Petra E ^b   Bullmore, Edward T ^b,c,d   Schafer, William R ^e   Nichols, Thomas E ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c ADDENBROOKE S HOSPITAL   (United Kingdom)

^d CAMBRIDGESHIRE AND PETERBOROUGH NHS FOUNDATION TRUST   (United Kingdom)

^e MRC LABORATORY OF MOLECULAR BIOLOGY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGICAL FUNCTIONS; CAENORHABDITIS ELEGANS; COMMUNITY STRUCTURE; CONNECTOME; ERDOS RENYI MIXTURE MODEL; LOCOMOTION; NERVE CELL; NERVOUS SYSTEM; NONHUMAN; STATISTICAL ANALYSIS; STOCHASTIC MODEL; SYNAPSE; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; MARKOV CHAIN; METABOLISM; NERVE CELL NETWORK;

ALGORITHMS; ANIMALS; CAENORHABDITIS ELEGANS; CONNECTOME; MODELS, BIOLOGICAL; NERVE NET; NEURONS; STOCHASTIC PROCESSES;

EID: 84903759143     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0097584     Document Type: Article

References (69)

1. Dicken P, Kelly PF, Olds K, Wai-Chung Yeung H (2002) Chains and networks, territories and scales: towards a relational framework for analysing the global economy. Global networks 1: 89-112.
2. Ulanowicz RE (2004) Quantitative methods for ecological network analysis. Computational Biology and Chemistry 28: 321-339.
3. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297: 1551-1555. (Pubitemid 34971002)
4. Albert R, Albert I, Nakarado GL (2004) Structural vulnerability of the north american power grid. Physical Review E 69: 025103.
5. Sporns O (2010) Networks of the Brain. MIT Press.
6. Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology 7: 113-140.
7. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proceedings of the National Academy of Sciences 104: 36-41. (Pubitemid 46075032)
8. Pan R, Chatterjee N, Sinha S (2010) Mesoscopic organization reveals the constraints governing Caenorhabditis elegans nervous system. PloS one 5: e9240.
9. Towlson EK, Vértes PE, Ahnert SE, Schafer WR, Bullmore ET (2013) The rich club of the C. elegans neuronal connectome. The Journal of Neuroscience 33: 6380-6387.
10. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10: 186-198.
11. Meunier D, Lambiotte R, Bullmore ET (2010) Modular and hierarchically modular organization of brain networks. Frontiers in neuroscience 4.
12. Goldenberg A, Zheng A, Fienberg S (2010) A survey of statistical network models. Now Publishers.
13. Fortunato S (2010) Community detection in graphs. Physics Reports 486: 75-174.
14. Blondel V, Guillaume J, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008: P10008.
15. Newman M (2006) Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103: 8577-8582.
16. Newman M (2004) Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38: 321-330.
17. Lorrain F, White HC (1971) Structural equivalence of individuals in social networks. The Journal of mathematical sociology 1: 49-80.
18. White HC, Boorman SA, Breiger RL (1976) Social structure from multiple networks. i. blockmodels of roles and positions. American journal of sociology : 730-780.
19. Doreian P, Batagelj V, Ferligoj A (2005) Generalized blockmodeling, volume 25. Cambridge Univ Pr.
20. Holland PW, Leinhardt S (1981) An exponential family of probability distributions for directed graphs. Journal of the american Statistical association 76: 33-50.
21. Wang Y, Wong G (1987) Stochastic blockmodels for directed graphs. Journal of the American Statistical Association : 8-19.
22. Anderson C, Wasserman S, Faust K (1992) Building stochastic blockmodels. Social Networks 14: 137-161.
23. Snijders T, Nowicki K (1997) Estimation and prediction for stochastic blockmodels for graphs with latent block structure. Journal of Classification 14: 75-100.
24. Daudin J, Picard F, Robin S (2008) A mixture model for random graphs. Statistics and computing 18: 173-183.
25. Borgatti SP, Everett MG (2000) Models of core/periphery structures. Social networks 21: 375-395.
26. White J, Southgate E, Thomson J, Brenner S (1986) The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London B, Biological Sciences 314: 1-340.
27. Chen B, Hall D, Chklovskii D (2006) Wiring optimization can relate neuronal structure and func- tion. Proceedings of the National Academy of Sciences of the United States of America 103: 4723-4728.
28. Varshney L, Chen B, Paniagua E, Hall D, Chklovskii D (2011) Structural properties of the Caenorhabditis elegans neuronal network. PLoS computational biology 7: e1001066.
29. Altun Z, Hall D (2005) Handbook of C. elegans anatomy. WormAtlas http://wwwwormatlasorg/handbook/contentshtm.
30. Haspel G, O'Donovan M, Hart A (2010) Motoneurons dedicated to either forward or backward locomotion in the nematode Caenorhabditis elegans. The Journal of Neuroscience 30: 11151-11156.
31. Wood WB, editor(1987) The nematode Caenorhabditis elegans. Cold Spring Harbour Laboratory.
32. Von Stetina SE, Treinin M, Miller D (2006) The motor circuit. Int Rev Neurobiol 69: 125-167. (Pubitemid 43588609)
33. Altun ZF HD (2009) Nervous system, general description. Worm atlas.
34. Varier S, Kaiser M (2011) Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network. PLoS Computational Biology 7: e1001044.
35. Brenner S (1974) The genetics of Caenorhabditis elegans. Genetics 77: 71-94.
36. Newman M, Watts D, Strogatz S (2002) Random graph models of social networks. Proceedings of the National Academy of Sciences of the United States of America 99: 2566-2572. (Pubitemid 34165067)
37. Erdo{combining double acute accent}s P, Rényi A (1959) On random graphs. Publicationes Mathematicae Debrecen 6: 290-297.
38. Gilbert E (1959) Random graphs. The Annals of Mathematical Statistics 30: 1141-1144.
39. Latouche P, Birmele E, Ambroise C (2008) Bayesian methods for graph clustering. SSB Research Report.
40. Zanghi H, Ambroise C, Miele V (2008) Fast online graph clustering via erdosrenyi mixture. Pattern Recognition 41: 3592-3599.
41. Zanghi H, Picard F, Miele V, Ambroise C (2008) Strategies for online inference of network mixture. Research publication.
42. Latouche P, Birmelé E, Ambroise C (2011) Overlapping stochastic block models with application to the french political blogosphere. The Annals of Applied Statistics 5: 309-336.
43. Jaakkola T (2001) 10 tutorial on variational approximation methods. Advanced mean field methods: theory and practice : 129.
44. Jordan M, Ghahramani Z, Jaakkola T, Saul L (1999) An introduction to variational methods for graphical models. Machine learning 37: 183-233. (Pubitemid 30544678)
45. Newman M, Girvan M (2004) Finding and evaluating community structure in networks. Physical review E 69: 026113.
46. Luczak T (1989) Sparse random graphs with a given degree sequence. In: Proceedings of the Symposium on Random Graphs, Poznan. pp. 165-182.
47. Molloy M, Reed B (1995) A critical point for random graphs with a given degree sequence. Random Structures & Algorithms 6: 161-180.
48. Pattison P, Robins G (2007) Handbook of probability theory with applications. chapter probabilistic network theory.
49. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52: 1059-1069.
50. Mukherjee S, Hill SM (2011) Network clustering: probing biological heterogeneity by sparse graphical models. Bioinformatics 27: 994-1000.
51. Handl J, Knowles J, Kell D (2005) Computational cluster validation in postgenomic data analysis. Bioinformatics 21: 3201-3212. (Pubitemid 41418432)
52. Hubert L, Arabie P (1985) Comparing partitions. Journal of classification 2: 193-218.
53. RandW(1971) Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association 66: 846-850.
54. Morey L, Agresti A (1984) The measurement of classification agreement: an adjustment to the rand statistic for chance agreement. Educational and Psychological Measurement 44: 33-37.
55. Dobson A (2001) An introduction to generalized linear models. Chapman & Hall/CRC.
56. Akaike H (1974) A new look at the statistical model identification. Automatic Control, IEEE Transactions on 19: 716-723.
57. Burnham KP, Anderson DR (2004) Multimodel inference understanding aic and bic in model selection. Sociological methods & research 33: 261-304. (Pubitemid 39519124)
58. Fraley C, Raftery AE (2006) MCLUST Version 3 for R: Normal Mixture Modeling and Model-based Clustering. (revised in 2012).
59. Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association 97: 611-631.
60. Bates D, Maechler M, Bolker B (2012) lme4: Linear mixed-effects models using S4 classes. URL http://CRAN.R-project.org/package=lme4. R package version 0.999999-0.
61. Harville D (1977) Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association 72: 320-338.
62. Ravasz E, Barabási AL (2003) Hierarchical organization in complex networks. Physical Review E 67: 026112.
63. Haspel G, O'Donovan MJ (2011) A perimotor framework reveals functional segmentation in the motoneuronal network controlling locomotion in caenorhabditis elegans. The Journal of Neuroscience 31: 14611-14623.
64. Alexander-Bloch AF, Vértes PE, Stidd R, Lalonde F, Clasen L, et al. (2013) The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. Cerebral Cortex 23: 127-138.
65. Holme P (2005) Core-periphery organization of complex networks. Physical Review E 72: 046111.
66. Royer L, Reimann M, Andreopoulos B, Schroeder M (2008) Unraveling protein networks with power graph analysis. PLoS Computational Biology 4: e1000108.
67. Vu DQ, Hunter DR, Schweinberger M (2012) Model-based clustering of large networks. arXiv preprint arXiv:12070188.
68. Karrer B, Newman ME (2011) Stochastic blockmodels and community structure in networks. Physical Review E 83: 016107.
69. Zachary WW (1977) An information ow model for conict and fission in small groups. Journal of Anthropological Research : 452-473.