Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Autoři: Caroline Hartley aff001;  Simon Farmer aff003;  Luc Berthouze aff004
Působiště autorů: Centre for Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, United Kingdom aff001;  Department of Paediatrics, University of Oxford, Oxford, United Kingdom aff002;  Institute of Neurology, University College London, London, United Kingdom aff003;  Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226772


Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain.

Klíčová slova:

Clustering coefficients – Depression – Electroencephalography – Infants – Network analysis – Neural networks – Neuronal plasticity – Neurons


1. Sporns O, Tononi G, Edelman GM. Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb Cortex. 2000;10(2):127–41. doi: 10.1093/cercor/10.2.127 10667981

2. van den Heuvel MP, Sporns O. Rich-club organization of the human connectome. J Neurosci. 2011;31(44):15775–86. doi: 10.1523/JNEUROSCI.3539-11.2011 22049421

3. van den Heuvel MP, Kersbergen KJ, de Reus MA, Keunen K, Kahn RS, Groenendaal F, et al. The neonatal connectome during preterm brain development. Cereb Cortex. 2015;25(9):3000–13. doi: 10.1093/cercor/bhu095 24833018

4. Ball G, Aljabar P, Zebari S, Tusor N, Arichi T, Merchant N, et al. Rich-club organization of the newborn human brain. Proc Natl Acad Sci U S A. 2014;111(20):7456–61. doi: 10.1073/pnas.1324118111 24799693

5. Kostović I, Jovanov-Milosević N. The development of cerebral connections during the first 20-45 weeks’ gestation. Semin Fetal Neonatal Med. 2006;11(6):415–22. doi: 10.1016/j.siny.2006.07.001 16962836

6. Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol. 1997;387(2):167–78. doi: 10.1002/(sici)1096-9861(19971020)387:2<167::aid-cne1>3.0.co;2-z 9336221

7. André M, Lamblin MD, d’Allest AM, Curzi-Dascalova L, Moussalli-Salefranque F, S Nguyen The T, et al. Electroencephalography in premature and full-term infants. Developmental features and glossary. Neurophysiol Clin. 2010;40(2):59–124. doi: 10.1016/j.neucli.2010.02.002 20510792

8. Colonnese MT, Kaminska A, Minlebaev M, Milh M, Bloem B, Lescure S, et al. A conserved switch in sensory processing prepares developing neocortex for vision. Neuron. 2010;67(3):480–98. doi: 10.1016/j.neuron.2010.07.015 20696384

9. Chipaux M, Colonnese MT, Mauguen A, Fellous L, Mokhtari M, Lezcano O, et al. Auditory stimuli mimicking ambient sounds drive temporal delta-brushes in premature infants. PLoS One. 2013;8(11):e79028. doi: 10.1371/journal.pone.0079028 24244408

10. Fabrizi L, Slater R, Worley A, Meek J, Boyd S, Olhede S, et al. A shift in sensory processing that enables the developing human brain to discriminate touch from pain. Curr Biol. 2011;21(18):1552–8. doi: 10.1016/j.cub.2011.08.010 21906948

11. Milh M, Kaminska A, Huon C, Lapillonne A, Ben-Ari Y, Khazipov R. Rapid cortical oscillations and early motor activity in premature human neonate. Cereb Cortex. 2007;17(7):1582–94. doi: 10.1093/cercor/bhl069 16950867

12. Dupont E, Hanganu IL, Kilb W, Hirsch S, Luhmann HJ. Rapid developmental switch in the mechanisms driving early cortical columnar networks. Nature. 2006;439(7072):79–83. doi: 10.1038/nature04264 16327778

13. Vanhatalo S, Lauronen L. Neonatal SEP—back to bedside with basic science. Semin Fetal Neonatal Med. 2006;11(6):464–70. doi: 10.1016/j.siny.2006.07.009 16978936

14. Kanold PO. Subplate neurons: crucial regulators of cortical development and plasticity. Front Neuroanat. 2009;3:16. doi: 10.3389/neuro.05.016.2009 19738926

15. Arichi T, Whitehead K, Barone G, Pressler R, Padormo F, Edwards AD, et al. Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI. Elife. 2017;6. doi: 10.7554/eLife.27814 28893378

16. Katz LC, Shatz CJ. Synaptic activity and the construction of cortical circuits. Science. 1996;274(5290):1133–8. doi: 10.1126/science.274.5290.1133 8895456

17. Tolner EA, Sheikh A, Yukin AY, Kaila K, Kanold PO. Subplate neurons promote spindle bursts and thalamocortical patterning in the neonatal rat somatosensory cortex. J Neurosci. 2012;32(2):692–702. doi: 10.1523/JNEUROSCI.1538-11.2012 22238105

18. Ghosh A, Shatz CJ. Involvement of subplate neurons in the formation of ocular dominance columns. Science. 1992;255(5050):1441–3.

19. Kanold PO, Kara P, Reid RC, Shatz CJ. Role of subplate neurons in functional maturation of visual cortical columns. Science. 2003;301(5632):521–5. doi: 10.1126/science.1084152 12881571

20. Schmidt JT, Eisele LE. Stroboscopic illumination and dark rearing block the sharpening of the regenerated retinotectal map in goldfish. Neuroscience. 1985;14(2):535–46. doi: 10.1016/0306-4522(85)90308-2 2986040

21. Weliky M, Katz LC. Disruption of orientation tuning in visual cortex by artificially correlated neuronal activity. Nature. 1997;386(6626):680–5. doi: 10.1038/386680a0 9109486

22. Piochon C, Kano M, Hansel C. LTD-like molecular pathways in developmental synaptic pruning. Nat Neurosci. 2016;19(10):1299–310. doi: 10.1038/nn.4389 27669991

23. Iyer KK, Roberts JA, Hellström-Westas L, Wikström S, Hansen Pupp I, Ley D, et al. Cortical burst dynamics predict clinical outcome early in extremely preterm infants. Brain. 2015;138(Pt 8):2206–18. doi: 10.1093/brain/awv129 26001723

24. Hartley C, Berthouze L, Mathieson SR, Boylan GB, Rennie JM, Marlow N, et al. Long-range temporal correlations in the EEG bursts of human preterm babies. PLoS One. 2012;7(2):e31543. doi: 10.1371/journal.pone.0031543 22363669

25. Ben-Ari Y. Excitatory actions of GABA during development: the nature of the nurture. Nat Rev Neurosci. 2002;3(9):728–39. doi: 10.1038/nrn920 12209121

26. Bhattacharya J, Edwards J, Mamelak AN, Schuman EM. Long-range temporal correlations in the spontaneous spiking of neurons in the hippocampal-amygdala complex of humans. Neuroscience. 2005;131(2):547–55. doi: 10.1016/j.neuroscience.2004.11.013 15708495

27. Turrigiano G. Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu Rev Neurosci. 2011;34:89–103. doi: 10.1146/annurev-neuro-060909-153238 21438687

28. Song S, Miller KD, Abbott LF. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci. 2000;3(9):919–26. doi: 10.1038/78829 10966623

29. Bassett DS, Bullmore E. Small-world brain networks. Neuroscientist. 2006;12(6):512–23. doi: 10.1177/1073858406293182 17079517

30. Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. Organization, development and function of complex brain networks. Trends Cogn Sci. 2004;8(9):418–25. doi: 10.1016/j.tics.2004.07.008 15350243

31. Sporns O. The non-random brain: efficiency, economy, and complex dynamics. Front Comput Neurosci. 2011;5:5. doi: 10.3389/fncom.2011.00005 21369354

32. Barabasi Albert. Emergence of scaling in random networks. Science. 1999;286(5439):509–12. doi: 10.1126/science.286.5439.509 10521342

33. Goodhill GJ, Gu M, Urbach JS. Predicting axonal response to molecular gradients with a computational model of filopodial dynamics. Neural Comput. 2004;16(11):2221–43. doi: 10.1162/0899766041941934 15476599

34. Kiddie G, McLean D, Van Ooyen A, Graham B. Biologically plausible models of neurite outgrowth. Prog Brain Res. 2005;147:67–80. doi: 10.1016/S0079-6123(04)47006-X 15581698

35. Van Ooyen A, Van Pelt J. Activity-dependent outgrowth of neurons and overshoot phenoman in developing neural networks. J Theor Biol. 1994;167:27–43. doi: 10.1006/jtbi.1994.1047

36. Meisel C, Gross T. Adaptive self-organization in a realistic neural network model. Physical Review E. 2009;80(6):061917. doi: 10.1103/PhysRevE.80.061917

37. Damicelli F, Hilgetag CC, Hütt MT, Messé A. Modular topology emerges from plasticity in a minimalistic excitable network model. Chaos. 2017;27(4):047406. doi: 10.1063/1.4979561 28456166

38. Hartley C, Taylor TJ, Kiss IZ, Farmer SF, Berthouze L. Identification of Criticality in Neuronal Avalanches: II. A Theoretical and Empirical Investigation of the Driven Case. J Math Neurosci. 2014;4:9. doi: 10.1186/2190-8567-4-9 24872924

39. Rose SE, Hatzigeorgiou X, Strudwick MW, Durbridge G, Davies PSW, Colditz PB. Altered white matter diffusion anisotropy in normal and preterm infants at term-equivalent age. Magn Reson Med. 2008;60(4):761–7. doi: 10.1002/mrm.21689 18816850

40. Vinall J, Miller SP, Bjornson BH, Fitzpatrick KPV, Poskitt KJ, Brant R, et al. Invasive procedures in preterm children: brain and cognitive development at school age. Pediatrics. 2014;133(3):412–21. doi: 10.1542/peds.2013-1863 24534406

41. Smyser CD, Snyder AZ, Shimony JS, Mitra A, Inder TE, Neil JJ. Resting-State Network Complexity and Magnitude Are Reduced in Prematurely Born Infants. Cereb Cortex. 2016;26(1):322–333. doi: 10.1093/cercor/bhu251 25331596

42. Damaraju E, Phillips JR, Lowe JR, Ohls R, Calhoun VD, Caprihan A. Resting-state functional connectivity differences in premature children. Front Syst Neurosci. 2010;4. doi: 10.3389/fnsys.2010.00023 20725534

43. Grunau RE. Neonatal pain in very preterm infants: long-term effects on brain, neurodevelopment and pain reactivity. Rambam Maimonides Med J. 2013;4(4):e0025. doi: 10.5041/RMMJ.10132 24228168

44. Wiegert JS, Oertner TG. Long-term depression triggers the selective elimination of weakly integrated synapses. Proc Natl Acad Sci U S A. 2013;110(47):E4510–9. doi: 10.1073/pnas.1315926110 24191047

45. Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry. Cereb Cortex. 2007;17(4):951–61. doi: 10.1093/cercor/bhl006 16772313

46. Rubinov M, Knock SA, Stam CJ, Micheloyannis S, Harris AWF, Williams LM, et al. Small-world properties of nonlinear brain activity in schizophrenia. Hum Brain Mapp. 2009;30(2):403–16. doi: 10.1002/hbm.20517 18072237

47. Supekar K, Uddin LQ, Khouzam A, Phillips J, Gaillard WD, Kenworthy LE, et al. Brain hyperconnectivity in children with autism and its links to social deficits. Cell Rep. 2013;5(3):738–47. doi: 10.1016/j.celrep.2013.10.001 24210821

48. Mostofsky SH, Powell SK, Simmonds DJ, Goldberg MC, Caffo B, Pekar JJ. Decreased connectivity and cerebellar activity in autism during motor task performance. Brain. 2009;132(Pt 9):2413–25. doi: 10.1093/brain/awp088 19389870

49. Hartley C, Berthouze L. Code for Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex.

50. Liu YH, Wang XJ. Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. J Comput Neurosci. 2001;10(1):25–45. doi: 10.1023/a:1008916026143 11316338

51. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82–7. doi: 10.1063/1.166141 11538314

52. Hu K, Ivanov PC, Chen Z, Carpena P, Stanley HE. Effect of trends on detrended fluctuation analysis. Phys Rev E Stat Nonlin Soft Matter Phys. 2001;64(1 Pt 1):011114. doi: 10.1103/PhysRevE.64.011114 11461232

53. Linkenkaer-Hansen K, Nikouline VV, Palva JM, Ilmoniemi RJ. Long-range temporal correlations and scaling behavior in human brain oscillations. J Neurosci. 2001;21(4):1370–7. doi: 10.1523/JNEUROSCI.21-04-01370.2001 11160408

54. Linkenkaer-Hansen K, Nikulin VV, Palva JM, Kaila K, Ilmoniemi RJ. Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations. Eur J Neurosci. 2004;19(1):203–11. doi: 10.1111/j.1460-9568.2004.03116.x 14750978

55. Nikulin VV, Brismar T. Long-range temporal correlations in alpha and beta oscillations: effect of arousal level and test-retest reliability. Clin Neurophysiol. 2004;115(8):1896–908. doi: 10.1016/j.clinph.2004.03.019 15261868

56. Nikulin VV, Brismar T. Long-range temporal correlations in electroencephalographic oscillations: Relation to topography, frequency band, age and gender. Neuroscience. 2005;130(2):549–58. doi: 10.1016/j.neuroscience.2004.10.007 15664711

57. Berthouze L, James LM, Farmer SF. Human EEG shows long-range temporal correlations of oscillation amplitude in Theta, Alpha and Beta bands across a wide age range. Clin Neurophysiol. 2010;121(8):1187–97. doi: 10.1016/j.clinph.2010.02.163 20346732

58. Smit DJA, de Geus EJC, van de Nieuwenhuijzen ME, van Beijsterveldt CEM, van Baal GCM, Mansvelder HD, et al. Scale-free modulation of resting-state neuronal oscillations reflects prolonged brain maturation in humans. J Neurosci. 2011;31(37):13128–36. doi: 10.1523/JNEUROSCI.1678-11.2011 21917796

59. Botcharova M, Farmer SF, Berthouze L. A maximum likelihood based technique for validating detrended fluctuation analysis (ML-DFA). arXiv. 2013; p. 1306.5075.

60. Ton R, Daffertshofer A. Model selection for identifying power-law scaling. Neuroimage. 2016;136:215–26. doi: 10.1016/j.neuroimage.2016.01.008 26774613

61. Benayoun M, Cowan JD, van Drongelen W, Wallace E. Avalanches in a stochastic model of spiking neurons. PLoS Comput Biol. 2010;6(7):e1000846. doi: 10.1371/journal.pcbi.1000846 20628615

62. Beggs JM, Plenz D. Neuronal avalanches in neocortical circuits. J Neurosci. 2003;23(35):11167–77. doi: 10.1523/JNEUROSCI.23-35-11167.2003 14657176

63. Beggs JM, Plenz D. Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J Neurosci. 2004;24(22):5216–29. doi: 10.1523/JNEUROSCI.0540-04.2004 15175392

64. Taylor TJ, Hartley C, Simon PL, Kiss IZ, Berthouze L. Identification of Criticality in Neuronal Avalanches: I. A Theoretical Investigation of the Non-driven Case. J Math Neurosci. 2013;3(1):5. doi: 10.1186/2190-8567-3-5 23618010

65. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69. doi: 10.1016/j.neuroimage.2009.10.003 19819337

66. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393(6684):440–2. doi: 10.1038/30918 9623998

67. Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc Natl Acad Sci U S A. 2006;103(51):19518–23. doi: 10.1073/pnas.0606005103 17159150

68. Humphries MD, Gurney K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One. 2008;3(4):e0002051. doi: 10.1371/journal.pone.0002051 18446219

69. Gritsun TA, le Feber J, Rutten WLC. Growth dynamics explain the development of spatiotemporal burst activity of young cultured neuronal networks in detail. PLoS One. 2012;7(9):e43352. doi: 10.1371/journal.pone.0043352 23028450

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