Table of Contents Author Guidelines Submit a Manuscript
Neural Plasticity
Volume 2016, Article ID 1478684, 13 pages
http://dx.doi.org/10.1155/2016/1478684
Review Article

Eyes Open on Sleep and Wake: In Vivo to In Silico Neural Networks

1Cyclotron Research Centre, University of Liège, 8 Allée du 6 Août, Bâtiment B30, 4000 Liège, Belgium
2Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Belgium
3Department of Electrical Engineering and Computer Science, University of Liège, 10 Allée de la Découverte, Bâtiment B28, 4000 Liège, Belgium

Received 23 July 2015; Accepted 11 October 2015

Academic Editor: Clive R. Bramham

Copyright © 2016 Amaury Vanvinckenroye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Cajochen, S. B. S. Khalsa, J. K. Wyatt, C. A. Czeisler, and D.-J. Dijk, “EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss,” The American Journal of Physiology—Regulatory Integrative and Comparative Physiology, vol. 277, no. 3, part 2, pp. R640–R649, 1999. View at Google Scholar · View at Scopus
  2. K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” NeuroImage, vol. 19, no. 4, pp. 1273–1302, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. W. D. Penny, K. J. Friston, J. Ashburner, S. Kiebel, and T. E. Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images, Academic Press, 2007.
  4. T. T. Dang-Vu, M. Desseilles, D. Petit, S. Mazza, J. Montplaisir, and P. Maquet, “Neuroimaging in sleep medicine,” Sleep Medicine, vol. 8, no. 4, pp. 349–372, 2007. View at Publisher · View at Google Scholar
  5. M. I. Garrido, J. M. Kilner, S. J. Kiebel, and K. J. Friston, “Evoked brain responses are generated by feedback loops,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 52, pp. 20961–20966, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. V. V. Vyazovskiy, U. Olcese, Y. M. Lazimy et al., “Cortical firing and sleep homeostasis,” Neuron, vol. 63, no. 6, pp. 865–878, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Aeschbach, J. R. Matthews, T. T. Postolache, M. A. Jackson, H. A. Giesen, and T. A. Wehr, “Dynamics of the human EEG during prolonged wakefulness: evidence for frequency-specific circadian and homeostatic influences,” Neuroscience Letters, vol. 239, no. 2-3, pp. 121–124, 1997. View at Publisher · View at Google Scholar · View at Scopus
  8. L. A. Finelli, H. Baumann, A. A. Borbély, and P. Achermann, “Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep,” Neuroscience, vol. 101, no. 3, pp. 523–529, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Vandewalle, S. N. Archer, C. Wuillaume et al., “Functional magnetic resonance imaging-assessed brain responses during an executive task depend on interaction of sleep homeostasis, circadian phase, and PER3 genotype,” Journal of Neuroscience, vol. 29, no. 25, pp. 7948–7956, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Bernard, Introduction à l'étude de la médecine expérimentale, Le Livre de Poche, 1865.
  11. A. A. Borbely, “A two process model of sleep regulation,” Human Neurobiology, vol. 1, no. 3, pp. 195–204, 1982. View at Google Scholar · View at Scopus
  12. R. Y. Moore, “Circadian rhythms: basic neurobiology and clinical applications,” Annual Review of Medicine, vol. 48, pp. 253–266, 1997. View at Publisher · View at Google Scholar · View at Scopus
  13. D.-J. Dijk and C. A. Czeisler, “Contribution of the circadian pacemaker and the sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans,” The Journal of Neuroscience, vol. 15, no. 5, part 1, pp. 3526–3538, 1995. View at Google Scholar · View at Scopus
  14. D.-J. Dijk and M. von Schantz, “Timing and consolidation of human sleep, wakefulness, and performance by a symphony of oscillators,” Journal of Biological Rhythms, vol. 20, no. 4, pp. 279–290, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Daunizeau, O. David, and K. E. Stephan, “Dynamic causal modelling: a critical review of the biophysical and statistical foundations,” NeuroImage, vol. 58, no. 2, pp. 312–322, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. B. T. Yeo, J. Tandi, and M. W. Chee, “Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation,” NeuroImage, vol. 111, pp. 147–158, 2015. View at Publisher · View at Google Scholar
  17. I. M. Verweij, N. Romeijn, D. J. A. Smit, G. Piantoni, E. J. W. Van Someren, and Y. D. van der Werf, “Sleep deprivation leads to a loss of functional connectivity in frontal brain regions,” BMC Neuroscience, vol. 15, no. 88, pp. 1471–2202, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Tononi and C. Cirelli, “Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration,” Neuron, vol. 81, no. 1, pp. 12–34, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Turrigiano, “Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function,” Cold Spring Harbor Perspectives in Biology, vol. 4, no. 1, Article ID a005736, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. M. G. Frank and R. Cantera, “Sleep, clocks, and synaptic plasticity,” Trends in Neurosciences, vol. 37, no. 9, pp. 491–501, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. V. V. Vyazovskiy, C. Cirelli, M. Pfister-Genskow, U. Faraguna, and G. Tononi, “Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep,” Nature Neuroscience, vol. 11, no. 2, pp. 200–208, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Huber, H. Mäki, M. Rosanova et al., “Human cortical excitability increases with time awake,” Cerebral Cortex, vol. 23, no. 2, pp. 332–338, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. Z.-W. Liu, U. Faraguna, C. Cirelli, G. Tononi, and X.-B. Gao, “Direct evidence for wake-related increases and sleep-related decreases in synaptic strength in rodent cortex,” Journal of Neuroscience, vol. 30, no. 25, pp. 8671–8675, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Moran, D. A. Pinotsis, and K. Friston, “Neural masses and fields in dynamic causal modeling,” Frontiers in Computational Neuroscience, vol. 7, article 57, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. P. A. Valdes-Sosa, A. Roebroeck, J. Daunizeau, and K. Friston, “Effective connectivity: influence, causality and biophysical modeling,” NeuroImage, vol. 58, no. 2, pp. 339–361, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. R. J. Ilmoniemi, J. Virtanen, J. Ruohonen et al., “Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity,” NeuroReport, vol. 8, no. 16, pp. 3537–3540, 1997. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Rosanova, S. Casarotto, A. Pigorini, P. Canali, A. G. Casali, and M. Massimini, “Combining transcranial magnetic stimulation with electroencephalography to study human cortical excitability and effective connectivity,” Neuromethods, vol. 67, pp. 435–457, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Pashut, S. Wolfus, A. Friedman et al., “Mechanisms of magnetic stimulation of central nervous system neurons,” PLoS Computational Biology, vol. 7, no. 3, Article ID e1002022, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. M. Massimini, F. Ferrarelli, R. Huber, S. K. Esser, H. Singh, and G. Tononi, “Breakdown of cortical effective connectivity during sleep,” Science, vol. 309, no. 5744, pp. 2228–2232, 2005. View at Google Scholar
  30. S. Crochet and C. C. H. Petersen, “Correlating whisker behavior with membrane potential in barrel cortex of awake mice,” Nature Neuroscience, vol. 9, no. 5, pp. 608–610, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. G. Buzsáki and A. Draguhn, “Neuronal oscillations in cortical networks,” Science, vol. 304, no. 5679, pp. 1926–1929, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Steriade and I. Timofeev, “Neuronal plasticity in thalamocortical networks during sleep and waking oscillations,” Neuron, vol. 37, no. 4, pp. 563–576, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Isomura, A. Sirota, S. Özen et al., “Integration and segregation of activity in entorhinal-hippocampal subregions by neocortical slow oscillations,” Neuron, vol. 52, no. 5, pp. 871–882, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. B. Tahvildari, M. Wölfel, A. Duque, and D. A. McCormick, “Selective functional interactions between excitatory and inhibitory cortical neurons and differential contribution to persistent activity of the slow oscillation,” Journal of Neuroscience, vol. 32, no. 35, pp. 12165–12179, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. J. S. Isaacson and M. Scanziani, “How inhibition shapes cortical activity,” Neuron, vol. 72, no. 2, pp. 231–243, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. M. N. Shadlen and W. T. Newsome, “Noise, neural codes and cortical organization,” Current Opinion in Neurobiology, vol. 4, no. 4, pp. 569–579, 1994. View at Publisher · View at Google Scholar · View at Scopus
  37. V. Kilman, M. C. W. van Rossum, and G. G. Turrigiano, “Activity deprivation reduces miniature IPSC amplitude by decreasing the number of postsynaptic GABAA receptors clustered at neocortical synapses,” The Journal of Neuroscience, vol. 22, no. 4, pp. 1328–1337, 2002. View at Google Scholar · View at Scopus
  38. E. Zagha and D. A. McCormick, “Neural control of brain state,” Current Opinion in Neurobiology, vol. 29, pp. 178–186, 2014. View at Publisher · View at Google Scholar
  39. M. B. Dash, C. L. Douglas, V. V. Vyazovskiy, C. Cirelli, and G. Tononi, “Long-term homeostasis of extracellular glutamate in the rat cerebral cortex across sleep and waking states,” Journal of Neuroscience, vol. 29, no. 3, pp. 620–629, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. D. V. Volgin, J. W. Lu, G. M. Stettner et al., “Time- and behavioral state-dependent changes in posterior hypothalamic GABAA receptors contribute to the regulation of sleep,” PLoS ONE, vol. 9, no. 1, Article ID e86545, 2014. View at Publisher · View at Google Scholar · View at Scopus
  41. V. V. Vyazovskiy, U. Olcese, E. C. Hanlon, Y. Nir, C. Cirelli, and G. Tononi, “Local sleep in awake rats,” Nature, vol. 472, no. 7344, pp. 443–447, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. Y. Nir, R. J. Staba, T. Andrillon et al., “Regional slow waves and spindles in human sleep,” Neuron, vol. 70, no. 1, pp. 153–169, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. L. Nobili, M. Ferrara, F. Moroni et al., “Dissociated wake-like and sleep-like electro-cortical activity during sleep,” NeuroImage, vol. 58, no. 2, pp. 612–619, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. C.-S. Hung, S. Sarasso, F. Ferrarelli et al., “Local experience-dependent changes in the wake EEG after prolonged wakefulness,” Sleep, vol. 36, no. 1, pp. 59–72, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. V. V. Vyazovskiy, U. Olcese, C. Cirelli, and G. Tononi, “Prolonged wakefulness alters neuronal responsiveness to local electrical stimulation of the neocortex in awake rats,” Journal of Sleep Research, vol. 22, no. 3, pp. 239–250, 2013. View at Publisher · View at Google Scholar · View at Scopus
  46. R. Tadavarty, P. S. Rajput, J. M. Wong, U. Kumar, and B. R. Sastry, “Sleep-deprivation induces changes in GABAB and mGlu receptor expression and has consequences for synaptic long-term depression,” PLoS ONE, vol. 6, no. 9, Article ID e24933, 2011. View at Publisher · View at Google Scholar
  47. G. Tononi and C. Cirelli, “Sleep and synaptic homeostasis: a hypothesis,” Brain Research Bulletin, vol. 62, no. 2, pp. 143–150, 2003. View at Publisher · View at Google Scholar · View at Scopus
  48. K. B. Hengen, M. E. Lambo, S. D. VanHooser, D. B. Katz, and G. G. Turrigiano, “Firing rate homeostasis in visual cortex of freely behaving rodents,” Neuron, vol. 80, no. 2, pp. 335–342, 2013. View at Publisher · View at Google Scholar · View at Scopus
  49. G. F. Gilestro, G. Tononi, and C. Cirelli, “Widespread changes in synaptic markers as a function of sleep and wakefulness in drosophila,” Science, vol. 324, no. 5923, pp. 109–112, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. S. Maret, U. Faraguna, A. B. Nelson, C. Cirelli, and G. Tononi, “Sleep and waking modulate spine turnover in the adolescent mouse cortex,” Nature Neuroscience, vol. 14, no. 11, pp. 1418–1420, 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. R. Huber, M. F. Ghilardi, M. Massimini, and G. Tononi, “Local sleep and learning,” Nature, vol. 430, no. 6995, pp. 78–81, 2004. View at Publisher · View at Google Scholar · View at Scopus
  52. D. Bushey, G. Tononi, and C. Cirelli, “Sleep and synaptic homeostasis: structural evidence in Drosophila,” Science, vol. 332, no. 6037, pp. 1576–1581, 2011. View at Publisher · View at Google Scholar · View at Scopus
  53. K. Hefti, S. C. Holst, J. Sovago et al., “Increased metabotropic glutamate receptor subtype 5 availability in human brain after one night without sleep,” Biological Psychiatry, vol. 73, no. 2, pp. 161–168, 2013. View at Publisher · View at Google Scholar · View at Scopus
  54. G. G. Turrigiano, “The self-tuning neuron: synaptic scaling of excitatory synapses,” Cell, vol. 135, no. 3, pp. 422–435, 2008. View at Publisher · View at Google Scholar · View at Scopus
  55. L. Appelbaum, G. Wang, T. Yokogawa et al., “Circadian and homeostatic regulation of structural synaptic plasticity in hypocretin neurons,” Neuron, vol. 68, no. 1, pp. 87–98, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. C. Liston, J. M. Cichon, F. Jeanneteau, Z. Jia, M. V. Chao, and W.-B. Gan, “Circadian glucocorticoid oscillations promote learning-dependent synapse formation and maintenance,” Nature Neuroscience, vol. 16, no. 6, pp. 698–705, 2013. View at Publisher · View at Google Scholar · View at Scopus
  57. L. Vollrath and I. Spiwoks-Becker, “Plasticity of retinal ribbon synapses,” Microscopy Research and Technique, vol. 35, no. 6, pp. 472–487, 1996. View at Google Scholar · View at Scopus
  58. J. A. McNulty, “Synaptic ribbons in the pineal organ of the goldfish: circadian rhythmicity and the effects of constant light and constant darkness,” Cell and Tissue Research, vol. 215, no. 3, pp. 491–497, 1981. View at Google Scholar · View at Scopus
  59. S. Ruiz, M. J. Ferreiro, K. I. Menhert, G. Casanova, A. Olivera, and R. Cantera, “Rhythmic changes in synapse numbers in Drosophila melanogaster motor terminals,” PLoS ONE, vol. 8, no. 6, Article ID e67161, 2013. View at Publisher · View at Google Scholar · View at Scopus
  60. N. Lang, H. Rothkegel, H. Reiber et al., “Circadian modulation of GABA-mediated cortical inhibition,” Cerebral Cortex, vol. 21, no. 10, pp. 2299–2306, 2011. View at Publisher · View at Google Scholar · View at Scopus
  61. H.-J. Park and K. Friston, “Structural and functional brain networks: from connections to cognition,” Science, vol. 342, no. 6158, Article ID 1238411, 2013. View at Publisher · View at Google Scholar · View at Scopus
  62. A. Devor, P. A. Bandettini, D. A. Boas et al., “The challenge of connecting the dots in the B.R.A.I.N,” Neuron, vol. 80, no. 2, pp. 270–274, 2013. View at Google Scholar
  63. O. Sporns, G. Tononi, and R. Kötter, “The human connectome: a structural description of the human brain,” PLoS computational biology, vol. 1, no. 4, article e42, 2005. View at Publisher · View at Google Scholar · View at Scopus
  64. P. Hagmann, M. Kurant, X. Gigandet et al., “Mapping human whole-brain structural networks with diffusion MRI,” PLoS ONE, vol. 2, no. 7, article e597, 2007. View at Publisher · View at Google Scholar · View at Scopus
  65. L. Cammoun, X. Gigandet, D. Meskaldji et al., “Mapping the human connectome at multiple scales with diffusion spectrum MRI,” Journal of Neuroscience Methods, vol. 203, no. 2, pp. 386–397, 2012. View at Publisher · View at Google Scholar · View at Scopus
  66. NIH, All about the human genome project (HGP).
  67. E. Ziegler, A. Foret, L. Mascetti et al., “Altered white matter architecture in BDNF met carriers,” PLoS ONE, vol. 8, no. 7, Article ID e69290, 2013. View at Publisher · View at Google Scholar · View at Scopus
  68. M. F. Egan, M. Kojima, J. H. Callicott et al., “The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function,” Cell, vol. 112, no. 2, pp. 257–269, 2003. View at Publisher · View at Google Scholar · View at Scopus
  69. L. Cao, A. Dhilla, J. Mukai et al., “Genetic modulation of BDNF signaling affects the outcome of axonal competition in vivo,” Current Biology, vol. 17, no. 11, pp. 911–921, 2007. View at Publisher · View at Google Scholar · View at Scopus
  70. O. Tymofiyeva, C. P. Hess, E. Ziv et al., “A DTI-based template-free cortical connectome study of brain maturation,” PLoS ONE, vol. 8, no. 5, Article ID e63310, 2013. View at Publisher · View at Google Scholar · View at Scopus
  71. T. T. Nakagawa, V. K. Jirsa, A. Spiegler, A. R. McIntosh, and G. Deco, “Bottom up modeling of the connectome: linking structure and function in the resting brain and their changes in aging,” NeuroImage, vol. 80, pp. 318–329, 2013. View at Publisher · View at Google Scholar · View at Scopus
  72. R. Liégeois, E. Ziegler, C. Phillips et al., “Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints,” Brain Structure and Function, 2015. View at Publisher · View at Google Scholar
  73. M. J. Brookes, M. Woolrich, H. Luckhoo et al., “Investigating the electrophysiological basis of resting state networks using magnetoencephalography,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 40, pp. 16783–16788, 2011. View at Publisher · View at Google Scholar · View at Scopus
  74. N. W. Falasca, S. D'Ascenzo, A. Di Domenico et al., “Hemispheric lateralization in top-down attention during spatial relation processing: a Granger causal model approach,” European Journal of Neuroscience, vol. 41, no. 7, pp. 914–924, 2015. View at Publisher · View at Google Scholar
  75. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar · View at Scopus
  76. A. K. Seth, A. B. Barrett, and L. Barnett, “Granger causality analysis in neuroscience and neuroimaging,” Journal of Neuroscience, vol. 35, no. 8, pp. 3293–3297, 2015. View at Publisher · View at Google Scholar
  77. A. Morrison, M. Diesmann, and W. Gerstner, “Phenomenological models of synaptic plasticity based on spike timing,” Biological Cybernetics, vol. 98, no. 6, pp. 459–478, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  78. N. J. Kopell, H. J. Gritton, M. A. Whittington, and M. A. Kramer, “Beyond the connectome: the dynome,” Neuron, vol. 83, no. 6, pp. 1319–1328, 2014. View at Publisher · View at Google Scholar · View at Scopus
  79. G. Buzsáki and X. J. Wang, “Mechanisms of gamma oscillations,” Annual Review of Neuroscience, vol. 35, no. 1, pp. 203–225, 2012. View at Publisher · View at Google Scholar
  80. R. T. Canolty, E. Edwards, S. S. Dalal et al., “High gamma power is phase-locked to theta oscillations in human neocortex,” Science, vol. 313, no. 5793, pp. 1626–1628, 2006. View at Publisher · View at Google Scholar · View at Scopus
  81. O. David and K. J. Friston, “A neural mass model for MEG/EEG: coupling and neuronal dynamics,” NeuroImage, vol. 20, no. 3, pp. 1743–1755, 2003. View at Publisher · View at Google Scholar · View at Scopus
  82. R. J. Moran, M. Symmonds, K. E. Stephan, K. J. Friston, and R. J. Dolan, “An in vivo assay of synaptic function mediating human cognition,” Current Biology, vol. 21, no. 15, pp. 1320–1325, 2011. View at Publisher · View at Google Scholar · View at Scopus
  83. M. Boly, M. I. Garrido, O. Gosseries et al., “Preserved feedforward but impaired top-down processes in the vegetative state,” Science, vol. 332, no. 6031, pp. 858–862, 2011. View at Publisher · View at Google Scholar · View at Scopus
  84. M. I. Garrido, J. M. Kilner, S. J. Kiebel, K. E. Stephan, T. Baldeweg, and K. J. Friston, “Repetition suppression and plasticity in the human brain,” NeuroImage, vol. 48, no. 1, pp. 269–279, 2009. View at Publisher · View at Google Scholar · View at Scopus
  85. W. Penny, S. Kiebel, and K. Friston, “Variational Bayesian inference for fMRI time series,” NeuroImage, vol. 19, no. 3, pp. 727–741, 2003. View at Publisher · View at Google Scholar · View at Scopus
  86. B. H. Jansen and V. G. Rit, “Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns,” Biological Cybernetics, vol. 73, no. 4, pp. 357–366, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  87. A. M. Bastos, W. M. Usrey, R. A. Adams, G. R. Mangun, P. Fries, and K. J. Friston, “Canonical microcircuits for predictive coding,” Neuron, vol. 76, no. 4, pp. 695–711, 2012. View at Publisher · View at Google Scholar · View at Scopus
  88. A. C. Marreiros, J. Daunizeau, S. J. Kiebel, and K. J. Friston, “Population dynamics: variance and the sigmoid activation function,” NeuroImage, vol. 42, no. 1, pp. 147–157, 2008. View at Publisher · View at Google Scholar · View at Scopus
  89. R. J. Moran, K. E. Stephan, T. Seidenbecher, H.-C. Pape, R. J. Dolan, and K. J. Friston, “Dynamic causal models of steady-state responses,” NeuroImage, vol. 44, no. 3, pp. 796–811, 2009. View at Publisher · View at Google Scholar · View at Scopus
  90. D. J. Felleman and D. C. Van Essen, “Distributed hierarchical processing in the primate cerebral cortex,” Cerebral Cortex, vol. 1, no. 1, pp. 1–47, 1991. View at Google Scholar · View at Scopus
  91. S. J. Kiebel, O. David, and K. J. Friston, “Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization,” NeuroImage, vol. 30, no. 4, pp. 1273–1284, 2006. View at Publisher · View at Google Scholar · View at Scopus
  92. K. Friston, R. Henson, C. Phillips, and J. Mattout, “Bayesian estimation of evoked and induced responses,” Human Brain Mapping, vol. 27, no. 9, pp. 722–735, 2006. View at Publisher · View at Google Scholar · View at Scopus
  93. W. D. Penny, K. E. Stephan, J. Daunizeau et al., “Comparing families of dynamic causal models,” PLoS Computational Biology, vol. 6, no. 3, Article ID e1000709, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  94. O. David, L. Harrison, and K. J. Friston, “Modelling event-related responses in the brain,” NeuroImage, vol. 25, no. 3, pp. 756–770, 2005. View at Publisher · View at Google Scholar · View at Scopus
  95. C. C. Chen, S. J. Kiebel, and K. J. Friston, “Dynamic causal modelling of induced responses,” NeuroImage, vol. 41, no. 4, pp. 1293–1312, 2008. View at Publisher · View at Google Scholar · View at Scopus
  96. W. D. Penny, V. Litvak, L. Fuentemilla, E. Duzel, and K. Friston, “Dynamic causal models for phase coupling,” Journal of Neuroscience Methods, vol. 183, no. 1, pp. 19–30, 2009. View at Publisher · View at Google Scholar · View at Scopus
  97. J. O'Neill, B. Pleydell-Bouverie, D. Dupret, and J. Csicsvari, “Play it again: reactivation of waking experience and memory,” Trends in Neurosciences, vol. 33, no. 5, pp. 220–229, 2010. View at Publisher · View at Google Scholar · View at Scopus
  98. C. Liégeois-Chauvel, A. Musolino, J. M. Badier, P. Marquis, and P. Chauvel, “Evoked potentials recorded from the auditory cortex in man: evaluation and topography of the middle latency components,” Electroencephalography and Clinical Neurophysiology, vol. 92, no. 3, pp. 204–214, 1994. View at Publisher · View at Google Scholar · View at Scopus
  99. S. Dehaene, J.-P. Changeux, L. Naccache, J. Sackur, and C. Sergent, “Conscious, preconscious, and subliminal processing: a testable taxonomy,” Trends in Cognitive Sciences, vol. 10, no. 5, pp. 204–211, 2006. View at Publisher · View at Google Scholar · View at Scopus
  100. M. Papadopoulou, M. Leite, P. van Mierlo et al., “Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy,” NeuroImage, vol. 107, pp. 117–126, 2015. View at Publisher · View at Google Scholar · View at Scopus
  101. M. T. Lazarewicz, R. S. Ehrlichman, C. R. Maxwell, M. J. Gandal, L. H. Finkel, and S. J. Siegel, “Ketamine modulates theta and gamma oscillations,” Journal of Cognitive Neuroscience, vol. 22, no. 7, pp. 1452–1464, 2010. View at Publisher · View at Google Scholar · View at Scopus
  102. R. J. Moran, M. W. Jones, A. J. Blockeel, R. A. Adams, K. E. Stephan, and K. J. Friston, “Losing control under ketamine: suppressed cortico-hippocampal drive following acute ketamine in rats,” Neuropsychopharmacology, vol. 40, no. 2, pp. 268–277, 2014. View at Publisher · View at Google Scholar · View at Scopus
  103. P. J. Uhlhaas and W. Singer, “Abnormal neural oscillations and synchrony in schizophrenia,” Nature Reviews Neuroscience, vol. 11, no. 2, pp. 100–113, 2010. View at Publisher · View at Google Scholar · View at Scopus
  104. W.-J. Gao and P. S. Goldman-Rakic, “Selective modulation of excitatory and inhibitory microcircuits by dopamine,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 5, pp. 2836–2841, 2003. View at Publisher · View at Google Scholar · View at Scopus
  105. V. K. Jirsa, W. C. Stacey, P. P. Quilichini, A. I. Ivanov, and C. Bernard, “On the nature of seizure dynamics,” Brain, vol. 137, no. 8, pp. 2210–2230, 2014. View at Publisher · View at Google Scholar · View at Scopus
  106. C. Allene, J. Lourenço, and A. Bacci, “The neuronal identity bias behind neocortical GABAergic plasticity,” Trends in Neurosciences, vol. 38, no. 9, pp. 524–534, 2015. View at Publisher · View at Google Scholar
  107. Y. Attal, M. Bhattacharjee, J. Yelnik et al., “Modeling and detecting deep brain activity with MEG & EEG,” Conference Proceedings: IEEE Engineering in Medicine and Biology Society, vol. 40, pp. 4937–4940, 2007. View at Google Scholar
  108. Y. Attal, B. Maess, A. Friederici, and O. David, “Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic data,” Reviews in the Neurosciences, vol. 23, no. 1, pp. 85–95, 2012. View at Publisher · View at Google Scholar · View at Scopus
  109. O. David, S. J. Kiebel, L. M. Harrison, J. Mattout, J. M. Kilner, and K. J. Friston, “Dynamic causal modeling of evoked responses in EEG and MEG,” NeuroImage, vol. 30, no. 4, pp. 1255–1272, 2006. View at Publisher · View at Google Scholar · View at Scopus
  110. M. Boly, R. Moran, M. Murphy et al., “Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness,” The Journal of Neuroscience, vol. 32, no. 20, pp. 7082–7090, 2012. View at Publisher · View at Google Scholar · View at Scopus
  111. A. C. Marreiros, H. Cagnan, R. J. Moran, K. J. Friston, and P. Brown, “Basal ganglia-cortical interactions in Parkinsonian patients,” NeuroImage, vol. 66, pp. 301–310, 2013. View at Publisher · View at Google Scholar · View at Scopus
  112. V. V. Vyazovskiy and K. D. Harris, “Sleep and the single neuron: the role of global slow oscillations in individual cell rest,” Nature Reviews Neuroscience, vol. 14, no. 6, pp. 443–451, 2013. View at Publisher · View at Google Scholar · View at Scopus
  113. L. Nobili, L. De Gennaro, P. Proserpio et al., “Local aspects of sleep: observations from intracerebral recordings in humans,” Progress in Brain Research, vol. 199, pp. 219–232, 2012. View at Publisher · View at Google Scholar · View at Scopus
  114. S. J. Kiebel, M. I. Garrido, R. Moran, C.-C. Chen, and K. J. Friston, “Dynamic causal modeling for EEG and MEG,” Human Brain Mapping, vol. 30, no. 6, pp. 1866–1876, 2009. View at Publisher · View at Google Scholar · View at Scopus
  115. D. A. Pinotsis, M. Leite, and K. J. Friston, “On conductance-based neural field models,” Frontiers in Computational Neuroscience, vol. 7, article 158, 2013. View at Publisher · View at Google Scholar · View at Scopus