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DCM, Conductance Based Models and Clinical Applications

  • A. C. MarreirosEmail author
  • D. A PinotsisEmail author
  • P. Brown
  • K. J. Friston
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 14)

Abstract

This chapter reviews some recent advances in dynamic causal modelling (DCM) of electrophysiology, in particular with respect to conductance based models and clinical applications. DCM addresses observed responses of complex neuronal systems by looking at the neuronal interactions that generate them and how these responses reflect the underlying neurobiology. DCM is a technique for inferring the biophysical properties of cortical sources and their directed connectivity based on distinct neuronal and observation models. The DCM framework uses mathematical formalisms of neural masses, neural fields and mean-fields as forward or generative models for observed neuronal activity. We here consider conductance based neural mass, mean-field and field models—and review their latest technical developments. We use dynamically rich conductance based models to generate responses in laminar-specific populations of excitatory and inhibitory cells. These models allow for the evaluation of neuronal connections and high-order statistics of neuronal states, using Bayesian estimation and inference. We also discuss recent clinical applications of DCM for convolution based neural mass models, in particular for the study of Parkinson’s disease. We present a study of data from Parkinsonian patients, and model the large-scale network changes underlying the pathological excess of beta oscillations that characterise the Parkinsonian state.

Keywords

Conductance based model Neural mass model Neural field model Dynamical causal modelling Model validation Electrophysiology EEG MEG LFP Parkinson’s disease Neuromodulation 

Abbreviations

BF

Bayes factor

BG

Basal ganglia nuclei

BMS

Bayesian model selection

DCM

Dynamic Causal Modelling

EEG

Electroencephalography

EM

Expectation-Maximization

ERP

Event-Related Potential

fMRI

Functional Magnetic Resonance Imaging

FN

FitzHugh-Nagumo

GLM

General linear model

GPe

Globus Pallidus externa

GPi

Globus Pallidus interna

JR

Jansen and Rit

HH

Hodgkin-Huxley

LFP

Local Field Potential

MEG

Magnetoencephalography

MFM

Mean field model

MM

Method of moments

MMN

Mismatch Negativity

MRI

Magnetic Resonance Imaging

NMM

Neural mass model

NFM

Neural field model

ODE

Ordinary differential equation

PD

Parkinson’s disease

SDE

Stochastic differential equation

SEP

Somatosensory evoked potential

SPM

Statistical parametric mapping

SSR

Steady state responses

STN

Subthalamic nucleus

Notes

Acknowledgments

ACM is funded by the Wellcome Trust and the Max-Planck Society, Tübingen. KJF and DAP are funded by the Wellcome Trust. PB is funded by the Medical Research Council UK, Wellcome Trust, Rosetrees Trust and the National Institute for Health Research Oxford Bio-medical Research Centre.

References

  1. 1.
    Marreiros AC, Stephan KE, Friston KJ. Dynamic causal modeling. Scholarpedia. 2010;5(7):9568.CrossRefGoogle Scholar
  2. 2.
    Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273–302.PubMedCrossRefGoogle Scholar
  3. 3.
    David O, Kiebel SJ, Harrison LM, Mattout J, Kilner JM, Friston KJ. Dynamic causal modeling of evoked responses in EEG and MEG. Neuroimage. 2006;30(4):1255–72. Epub 9 Feb 2006 .PubMedCrossRefGoogle Scholar
  4. 4.
    Moran RJ, Stephan KE, Seidenbecher T, Pape HC, Dolan RJ, Friston KJ. Dynamic causal models of steady-state responses. Neuroimage. 2009;44(3):796–811. doi: 10.1016/j.neuroimage.2008.09.048. Epub 17 Oct 2008 .PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Pinotsis DA, Moran RJ, Friston KJ. Dynamic causal modeling with neural fields. Neuroimage. 2012 Jan 16;59(2):1261–74. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3236998&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014. (Elsevier Inc.).PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Noppeney U, Josephs O, Hocking J, Price CJ, Friston KJ. The effect of prior visual information on recognition of speech and sounds. Cereb Cortex. 2008;18(3):598–609. http://www.ncbi.nlm.nih.gov/pubmed/17617658. Accessed 9 Jan 2014.PubMedCrossRefGoogle Scholar
  7. 7.
    Schofield TM, Iverson P, Kiebel SJ, Stephan KE, Kilner JM, Friston KJ, et al. Changing meaning causes coupling changes within higher levels of the cortical hierarchy. Proc Natl Acad Sci U S A. 2009;106:11765–70.PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Hartwigsen G, Saur D, Price CJ, Ulmer S, Baumgaertner A. Perturbation of the left inferior frontal gyrus triggers adaptive plasticity in the right homologous area during speech production. Proc Natl Acad Sci U S A. 2013;110(41):16402–7.PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Parkinson AL, Korzyukov O, Larson CR, Litvak V, Robin DA. Modulation of effective connectivity during vocalization with perturbed auditory feedback. Neuropsychologia. 2013;51(8):1471–80. http://www.ncbi.nlm.nih.gov/pubmed/23665378. Accessed 9 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Woodhead ZVJ, Barnes GR, Penny W, Moran R, Teki S, Price CJ, et al. Reading front to back: MEG evidence for early feedback effects during word recognition. Cereb Cortex. 2014 ;24(3):817–25. doi: 10.1093/cercor/bhs365. Epub 21 Nov 2012.Google Scholar
  11. 11.
    Garrido MI, Friston KJ, Kiebel SJ, Stephan KE, Baldeweg T, Kilner JM. The functional anatomy of the MMN: a DCM study of the roving paradigm. Neuroimage. 2008;42(2):936–44. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2640481&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Kiebel SJ, Garrido MI, Moran R, Chen C-C, Friston KJ. Dynamic causal modeling for EEG and MEG. Hum Brain Mapp. 2009;30(6):1866–76. http://www.ncbi.nlm.nih.gov/pubmed/19360734. Accessed 9 Jan 2014.PubMedCrossRefGoogle Scholar
  13. 13.
    Auksztulewicz R, Spitzer B, Blankenburg F. Recurrent neural processing and somatosensory awareness. J Neurosci. 2012;32(3):799–805. http://www.ncbi.nlm.nih.gov/pubmed/22262878. Accessed 19 Jan 2014.PubMedCrossRefGoogle Scholar
  14. 14.
    Auksztulewicz R, Blankenburg F. Subjective rating of weak tactile stimuli is parametrically encoded in event-related potentials. J Neurosci. 2013;33(29):11878–87. http://www.ncbi.nlm.nih.gov/pubmed/23864677. Accessed 19 Jan 2014.PubMedCrossRefGoogle Scholar
  15. 15.
    Shigihara Y, Zeki S. Parallelism in the brain’s visual form system. Eur J Neurosci. 2013;38(12):3712–20. http://www.ncbi.nlm.nih.gov/pubmed/24118503. Accessed 19 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Vossel S, Weidner R, Driver J, Friston KJ, Fink GR. Deconstructing the architecture of dorsal and ventral attention systems with dynamic causal modeling. J Neurosci. 2012;32(31):10637–48. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3432566&tool=pmcentrez&rendertype=abstract. Accessed 17 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Cárdenas-Morales L, Volz LJ, Michely J, Rehme AK, Pool E-M, Nettekoven C, et al. Network connectivity and individual responses to brain stimulation in the human motor system. Cereb Cortex. 2013;24(7):1697–707. doi: 10.1093/cercor/bht023. Epub 8 Feb 2013.Google Scholar
  18. 18.
    Van Wijk BCM, Litvak V, Friston KJ, Daffertshofer A. Nonlinear coupling between occipital and motor cortex during motor imagery: a dynamic causal modeling study. Neuroimage. 2013;71:104–13. http://www.ncbi.nlm.nih.gov/pubmed/23313570. Accessed 19 Jan 2014. (Elsevier Inc.)PubMedCrossRefGoogle Scholar
  19. 19.
    Penny WD, Litvak V, Fuentemilla L, Duzel E, Friston K. Dynamic causal models for phase coupling. J Neurosci Methods. 2009;183(1):19–30. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2751835&tool=pmcentrez&rendertype=abstract. Accessed 16 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Moran RJ, Symmonds M, Stephan KE, Friston KJ, Dolan RJ. An in vivo assay of synaptic function mediating human cognition. Curr Biol. 2011;21:1320–5.PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Penny WD, Stephan KE, Mechelli A, Friston KJ. Comparing dynamic causal models. Neuroimage. 2004;22(3):1157–72.PubMedCrossRefGoogle Scholar
  22. 22.
    Stephan KE, Penny WD, Daunizeau J, Moran RJ, Friston KJ. Bayesian model selection for group studies. Neuroimage. 2009;46:1004–17. doi: 10.1016/j.neuroimage.2009.03.025. Epub 20 Mar 2009PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Chen CC, Kiebel SJ, Friston KJ. Dynamic causal modelling of induced responses. Neuroimage. 2008;41(4):1293–312. http://www.ncbi.nlm.nih.gov/pubmed/18485744. Accessed 9 Jan 2014.PubMedCrossRefGoogle Scholar
  24. 24.
    Marreiros AC, Kiebel SJ, Daunizeau J, Harrison LM, Friston KJ. Population dynamics under the laplace assumption. Neuroimage. 2009;44:701–14.PubMedCrossRefGoogle Scholar
  25. 25.
    Friston KJ, Bastos A, Litvak V, Stephan KE, Fries P, Moran RJ. DCM for complex-valued data: cross-spectra, coherence and phase-delays. Neuroimage. 2012;59(1):439–55. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3200431&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014. (Elsevier Inc.)PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Pinotsis DA, Schwarzkopf DS, Litvak V, Rees G, Barnes G, Friston KJ. Dynamic causal modelling of lateral interactions in the visual cortex. Neuroimage. 2012;66C:563–76. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3547173&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014.Google Scholar
  27. 27.
    Pinotsis DA, Brunet N, Bastos A, Bosman CA, Litvak V, Fries P, et al. Contrast gain-control and horizontal interactions in V1: A DCM study. Neuroimage. 2014;92:143–55. doi: 10.1016/j.neuroimage.2014.01.047. Epub 2 Feb 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Daunizeau J, David O, Stephan KE. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage. 2011;58(2):312–22. doi: 10.1016/j.neuroimage.2009.11.062. Epub Dec 1 2009.PubMedCrossRefGoogle Scholar
  29. 29.
    Moran R, Pinotsis DA, Friston K. Neural masses and fields in dynamic causal modeling. Front Comput Neurosci. 2013;7(May):57. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3664834&tool=pmcentrez&rendertype=abstract. Accessed 14 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Pinotsis DA, Friston KJ. Neural fields, masses and bayesian modelling. In: Coombes S, Graben PB, Potthast R, Wright JJ, editors. Neural Field Theory. Mathematical Neuroscience Series. Springer; 2014. doi: 10.13140/2.1.2100.3209.Google Scholar
  31. 31.
    Pinotsis DA, Friston KJ. Extracting novel information from neuroimaging data using neural fields. EPJ Nonlin Biomed Phys. 2014;2: 5.Google Scholar
  32. 32.
    David O, Friston KJ. A neural-mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage. 2003;20(3):1743–55.Google Scholar
  33. 33.
    David O, Cosmelli D, Friston KJ. Evaluation of different measures of functional connectivity using a neural-mass model. NeuroImage. 2004;21(2):659–73.Google Scholar
  34. 34.
    Lee L., Friston K., Horwitz B. Large-scale neural models and dynamic causal modelling. NeuroImage. 2006;30:1243–1254.Google Scholar
  35. 35.
    Garrido MI, Friston KJ, Kiebel SJ, Stephan KE, Baldeweg T, Kilner JM. The functional anatomy of the MMN: a DCM study of the roving paradigm. NeuroImage. 2008;42(2):936–44.Google Scholar
  36. 36.
    Regan, D., Spekreijse H. Electrophysiological correlate of binocular depth perception in man. Nature. 1970;225(5227):92–4. http://dx.doi.org/10.1038/225092a0. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  37. 37.
    Kutas M, Hillyard SA. Electrophysiology of cognitive processing. Annu Rev Psychol. 1983;34:33–61. http://www.annualreviews.org/doi/pdf/10.1146/annurev.ps.34.020183.000341. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  38. 38.
    Baillet S, Mosher JC, Leahy RM. Electromagnetic brain mapping. IEEE Signal Process Mag. 2001;18(6):14–30. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=962275. Accessed 19 March 2014.CrossRefGoogle Scholar
  39. 39.
    Friston K. A theory of cortical responses. Philos Trans R Soc Lond B Biol Sci. 2005;360(1456):815–36. http://rstb.royalsocietypublishing.org/content/360/1456/815.short. Accessed 19 March 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci. 2010;11(2):127–38. http://dx.doi.org/10.1038/nrn2787. Accessed 19 March 2014. (Nat Publishing Group).PubMedCrossRefGoogle Scholar
  41. 41.
    Lopes da Silva FH, Hoeks A, Smits H, Zetterberg LH. Model of brain rhythmic activity. The alpha-rhythm of the thalamus. Kybernetik. 1974;15:27–37.PubMedCrossRefGoogle Scholar
  42. 42.
    Zetterberg LH, Kristiansson L, Mossberg K. Performance of a model for a local neuron population. Biol Cybern. 1978;31:15–26.PubMedCrossRefGoogle Scholar
  43. 43.
    Elbert T, Ray WJ, Kowalik ZJ, Skinner JE, Graf KE, Birbaumer N. Chaos and physiology: deterministic chaos in excitable cell assemblies. Physiol Rev. 1994;74:1–47.PubMedGoogle Scholar
  44. 44.
    Jansen BH, Rit VG. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol Cybern. 1995;73:357–66.PubMedCrossRefGoogle Scholar
  45. 45.
    Kincses WE, Braun C, Kaiser S, Elbert T. Modeling extended sources of event-related potentials using anatomical and physiological constraints. Hum Brain Mapp. 1999;8:182–93.PubMedCrossRefGoogle Scholar
  46. 46.
    Wendling F, Bellanger JJ, Bartolomei F, Chauvel P. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol Cybern. 2000;83:367–78.PubMedCrossRefGoogle Scholar
  47. 47.
    David O, Harrison L, Friston KJ. Modelling event-related responses in the brain. Neuroimage. 2005;25(3):756–70. http://www.ncbi.nlm.nih.gov/pubmed/15808977. (Elsevier).PubMedCrossRefGoogle Scholar
  48. 48.
    Valdes PA, Jimenez JC, Riera J, Biscay R, Ozaki T. Nonlinear EEG analysis based on a neural mass model. Biol Cybern. 1999;81(5–6):415–24. http://www.ncbi.nlm.nih.gov/pubmed/10592017. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  49. 49.
    Jansen BH, Kavaipatti AB, Markusson O. Evoked potential enhancement using a neurophysiologically-based model. Methods Inf Med. 2001;40(4):338–45. http://www.ncbi.nlm.nih.gov/pubmed/11552347. Accessed 30 March 2014.PubMedGoogle Scholar
  50. 50.
    Brown HR, Friston KJ. The functional anatomy of attention: a DCM study. Front Hum Neurosci. 2013;7(December):784. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3845206&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014.PubMedCentralPubMedGoogle Scholar
  51. 51.
    Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex. 1991;1:1–47.PubMedCrossRefGoogle Scholar
  52. 52.
    Kiebel S, David O, Friston K. Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization. Neuroimage. 2006;30(4):1273–84. Epub 21 Feb 2006.PubMedCrossRefGoogle Scholar
  53. 53.
    Friston KJ. Bayesian estimation of dynamical systems: an application to fMRI. Neuroimage. 2002;16:513–30.PubMedCrossRefGoogle Scholar
  54. 54.
    Izhikevich EM. Which model to use for cortical spiking neurons? IEEE Trans Neural Netw. 2004;15:1063–70.PubMedCrossRefGoogle Scholar
  55. 55.
    De Groff D, Neelakanta PS, Sudhakar R, Aalo V. Stochastical aspects of neuronal dynamics: fokker-planck approach. Biol Cybern. 1993;69:155–64.PubMedCrossRefGoogle Scholar
  56. 56.
    Nykamp DQ, Tranchina D. A population density approach that facilitates large-scale modeling of neural networks: extension to slow inhibitory synapses. Neural Comput. 2001;13:511–46.PubMedCrossRefGoogle Scholar
  57. 57.
    Casti ARR, Omurtag A, Sornborger A, Kaplan E, Knight B, Victor J, et al. A population study of integrate-and-fire-or-burst neurons. Neural Comput. 2002;14:957–86.PubMedCrossRefGoogle Scholar
  58. 58.
    Haskell E, Nykamp DQ, Tranchina D. Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size. Network. 2001;12:141–74.PubMedCrossRefGoogle Scholar
  59. 59.
    Knight BW. Dynamics of encoding in neuron populations: some general mathematical features. Neural Comput. 2000;12:473–518.PubMedCrossRefGoogle Scholar
  60. 60.
    Omurtag A, Knight BW, Sirovich L. On the simulation of large populations of neurons. J Comput Neurosci. 2000;8:51–63.PubMedCrossRefGoogle Scholar
  61. 61.
    Sirovich L. Dynamics of neuronal populations: eigenfunction theory; some solvable cases. Network. 2003;14:249–72.PubMedCrossRefGoogle Scholar
  62. 62.
    Chizhov AV, Graham LJ. Population model of hippocampal pyramidal neurons, linking a refractory density approach to conductance-based neurons. Phys Rev E Stat Nonlin Soft Matter Phys. 2007;75:011924.PubMedCrossRefGoogle Scholar
  63. 63.
    Rodriguez R, Tuckwell HC. Statistical properties of stochastic nonlinear dynamical models of single spiking neurons and neural networks. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1996;54(5):5585–90.CrossRefGoogle Scholar
  64. 64.
    Rodriguez RC, Tuckwell H. Noisy spiking neurons and networks: useful approximations for firing probabilities and global behavior. Bio Systems. 1998;48:187–94.PubMedCrossRefGoogle Scholar
  65. 65.
    Hasegawa H. Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises. Phys Rev E Stat Nonlin Soft Matter Phys. 2003;67:041903.PubMedCrossRefGoogle Scholar
  66. 66.
    Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput Biol. 2008;4:e1000092.PubMedCentralPubMedCrossRefGoogle Scholar
  67. 67.
    Freeman WJ. Mass action in the nervous system. New York: Academic;1975.Google Scholar
  68. 68.
    Marreiros AC, Daunizeau J, Kiebel SJ, Friston KJ. Population dynamics: variance and the sigmoid activation function. Neuroimage. 2008;42:147–57.PubMedCrossRefGoogle Scholar
  69. 69.
    Morris C, Lecar H. Voltage oscillations in the barnacle giant muscle fiber. Biophys J. 1981;35:193–213.PubMedCentralPubMedCrossRefGoogle Scholar
  70. 70.
    Harrison LM, David O, Friston KJ. Stochastic models of neuronal dynamics. Philos Trans R Soc Lond B Biol Sci. 2005;360:1075–91.PubMedCentralPubMedCrossRefGoogle Scholar
  71. 71.
    Tuckwell HC, Rodriguez R. Analytical and simulation results for stochastic fitzhugh-nagumo neurons and neural networks. J Comput Neurosci. 1998;5:91–113.PubMedCrossRefGoogle Scholar
  72. 72.
    Rodriguez R, Tuckwel HC. A dynamical system for the approximate moments of nonlinear stochastic models of spiking neurons and networks. Math Comput Model 2000;31:175–80.CrossRefGoogle Scholar
  73. 73.
    Hasegawa H. Dynamical mean-field theory of noisy spiking neuron ensembles: application to the hodgkin-huxley model. Phys Rev E Stat Nonlin Soft Matter Phys. 2003;68:041909.PubMedCrossRefGoogle Scholar
  74. 74.
    Marreiros AC, Kiebel SJ, Friston KJ. A dynamic causal model study of neuronal population dynamics. Neuroimage. 2010;51:91–101.PubMedCentralPubMedCrossRefGoogle Scholar
  75. 75.
    Stephan KE, Penny WD, Daunizeau J, Moran RJ, Friston KJ. Bayesian model selection for group studies. Neuroimage. 2009;46(4):1004–17. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2703732&tool=pmcentrez&rendertype=abstract. Accessed 21 March 2014. (Elsevier Inc.).PubMedCentralPubMedCrossRefGoogle Scholar
  76. 76.
    Moran RJ, Stephan KE, Dolan RJ, Friston KJ. Consistent spectral predictors for dynamic causal models of steady-state responses. Neuroimage. 2011;55(4):1694–708. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3093618&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014. (Elsevier Inc.).PubMedCentralPubMedCrossRefGoogle Scholar
  77. 77.
    Brunel N, Wang XJ. Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J Comput Neurosci. 2001;11(1):63–85. http://www.ncbi.nlm.nih.gov/pubmed/11524578. Accessed 28 March 2014.PubMedCrossRefGoogle Scholar
  78. 78.
    Goldman-Rakic PS. Regional and cellular fractionation of working memory. Proc Natl Acad Sci U S A. 1996;93:13473–80.PubMedCentralPubMedCrossRefGoogle Scholar
  79. 79.
    Durstewitz D, Seamans JK, Sejnowski TJ. Neurocomputational models of working memory. Nat Neurosci. 2000;3 Suppl:1184–91.PubMedCrossRefGoogle Scholar
  80. 80.
    Gorelova NA, Yang CR. Dopamine D1/D5 receptor activation modulates a persistent sodium current in rat prefrontal cortical neurons in vitro. J Neurophysiol. 2000;84:75–87.PubMedGoogle Scholar
  81. 81.
    Gonzalez-Islas C, Hablitz JJ. Dopamine enhances EPSCs in layer II-III pyramidal neurons in rat prefrontal cortex. J Neurosci. 2003;23:867–75.PubMedGoogle Scholar
  82. 82.
    Durstewitz D, Seamans JK. The dual-state theory of prefrontal cortex dopamine function with relevance to catechol-O-methyltransferase genotypes and schizophrenia. Biolo Psychiatry. 2008;64:739–49.CrossRefGoogle Scholar
  83. 83.
    Nunez PL. The brain wave equation: a model for the EEG. Math Biosci. 1974;21:279–97.CrossRefGoogle Scholar
  84. 84.
    Amari S. Homogeneous nets of neuron-like elements. Biol Cybern. 1975;17(4):211–20.PubMedCrossRefGoogle Scholar
  85. 85.
    Amari S. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern. 1977;27(2):77–87.PubMedCrossRefGoogle Scholar
  86. 86.
    Breakspear M, Terry JR, Friston KJ. Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics. Network. 2003;14(4):703–32.PubMedCrossRefGoogle Scholar
  87. 87.
    Liley DTJ, Bojak I. Understanding the transition to seizure by modeling the epileptiform activity of general anesthetic agents. J Clin Neurophysiol. 2005;22(5):300–13.PubMedGoogle Scholar
  88. 88.
    Pinotsis DA, Leite M, Friston KJ. On conductance-based neural field models. Front Comput Neurosci. 2013;7(November):158. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3824089&tool=pmcentrez&rendertype=abstract. Accessed 14 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  89. 89.
    Wilson HR, Cowan JD. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J. 1972;12(1):1–24. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1484078&tool=pmcentrez&rendertype=abstract. Accessed 30 March 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  90. 90.
    Jirsa V, Haken H. Field theory of electromagnetic brain activity. Phys Rev Lett. 1996;77:960–3.PubMedCrossRefGoogle Scholar
  91. 91.
    Buice MA, Cowan JD, Chow CC. Systematic fluctuation expansion for neural network activity equations. Neural Comput. 2010;22:377–426.PubMedCentralPubMedCrossRefGoogle Scholar
  92. 92.
    Touboul JD, Ermentrout GB. Finite-size and correlation-induced effects in mean-field dynamics. J Comput Neurosci. 2011;31(3):453–84. http://www.ncbi.nlm.nih.gov/pubmed/21384156. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  93. 93.
    Rose RM, Hindmarsh JL. The assembly of ionic currents in a thalamic neuron. I. The three-dimensional model. Proc R Soc Lond B Biol Sci. 1989;237(1288):267–88. http://www.ncbi.nlm.nih.gov/pubmed/2571154. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  94. 94.
    Wilson MT, Robinson PA, O’Neill B, Steyn-Ross DA. Complementarity of spike- and rate-based dynamics of neural systems. PLoS Comput Biol. 2012;8(6):e1002560. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3380910&tool=pmcentrez&rendertype=abstract. Accessed 30 March 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  95. 95.
    Robinson PA, Kim JW. Spike, rate, field, and hybrid methods for treating neuronal dynamics and interactions. J Neurosci Methods. 2012;205(2):283–94. http://www.ncbi.nlm.nih.gov/pubmed/22330795. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  96. 96.
    Liley DTJ, Cadusch PJ, Gray M, Nathan PJ. Drug-induced modification of the system properties associated with spontaneous human electroencephalographic activity. Phys Rev E Stat Nonlin Soft Matter Phys. 2003;68(5 Pt 1):051906. http://www.ncbi.nlm.nih.gov/pubmed/14682819. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  97. 97.
    Bojak I, Liley DTJ. Modeling the effects of anesthesia on the electroencephalogram. Phys Rev E Stat Nonlin Soft Matter Phys. 2005;71(4 Pt 1):041902. http://www.ncbi.nlm.nih.gov/pubmed/15903696. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  98. 98.
    Steyn-Ross ML, Steyn-Ross DA, Sleigh JW, Wilcocks LC. Toward a theory of the general-anesthetic-induced phase transition of the cerebral cortex. I. A thermodynamics analogy. Phys Rev E Stat Nonlin Soft Matter Phys. 2001;64(1 Pt 1):011917. http://www.ncbi.nlm.nih.gov/pubmed/11461298. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  99. 99.
    Steyn-Ross DA, Steyn-Ross ML, Sleigh JW, Wilson MT. Progress in modeling EEG effects of general anesthesia: biphasic response and hysteresis. New York: Springer; 2011.Google Scholar
  100. 100.
    Wilson MT, Sleigh JW, Steyn-Ross DA, Steyn-Ross ML. General anesthetic-induced seizures can be explained by a mean-field model of cortical dynamics. Anesthesiology. 2006;104(3):588–93. http://www.ncbi.nlm.nih.gov/pubmed/16508406. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  101. 101.
    Goldstein SS, Rall W. Changes of action potential shape and velocity for changing core conductor geometry. Biophys J. 1974;14(10):731–57. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1334570&tool=pmcentrez&rendertype=abstract. Accessed 27 March 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  102. 102.
    Ermentrout B. Neural networks as spatio-temporal pattern-forming systems. Rep Prog Phys. 1998;61:353–430. http://iopscience.iop.org/0034-4885/61/4/002/pdf/0034-4885_61_4_002.pdf. Accessed 30 March 2014.CrossRefGoogle Scholar
  103. 103.
    Somers DC, Nelson SB, Sur M. An emergent model of orientation selectivity in cat visual cortical simple cells. J Neurosci. 1995;15(8):5448–65. http://www.ncbi.nlm.nih.gov/pubmed/7643194. Accessed 30 March 2014.PubMedGoogle Scholar
  104. 104.
    Hutt A, Longtin A. Effects of the anesthetic agent propofol on neural populations. Cogn Neurodyn. 2010;4(1):37–59. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2837528&tool=pmcentrez&rendertype=abstract. Accessed 30 March 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  105. 105.
    Moran RJ, Stephan KE, Kiebel SJ, Rombach N, O’Connor WT, Murphy KJ, et al. Bayesian estimation of synaptic physiology from the spectral responses of neural masses. Neuroimage. 2008;42:272–84.PubMedCentralPubMedCrossRefGoogle Scholar
  106. 106.
    Traub RD. Fast oscillations in cortical circuits. Cereb Cortex. 2014;24(11):2873–83. doi: 10.1093/cercor/bht140. Epub 2 Jun 2013.Google Scholar
  107. 107.
    Roiser JP, Wigton R, Kilner JM, Mendez MA, Hon N, Friston KJ, et al. Dysconnectivity in the frontoparietal attention network in schizophrenia. Front psychiatry. 2013;4(December):176. http://www.ncbi.nlm.nih.gov/pubmed/24399975. Accessed 14 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  108. 108.
    Dima D, Dietrich DE, Dillo W, Emrich HM. Impaired top-down processes in schizophrenia: a DCM study of ERPs. Neuroimage. 2010;52(3):824–32. http://www.ncbi.nlm.nih.gov/pubmed/20056155. Accessed 9 Jan 2014. (Elsevier Inc).PubMedCrossRefGoogle Scholar
  109. 109.
    Dima D, Frangou S, Burge L, Braeutigam S, James AC. Abnormal intrinsic and extrinsic connectivity within the magnetic mismatch negativity brain network in schizophrenia: a preliminary study. Schizophr Res. 2012;135(1–3):23–7. http://www.ncbi.nlm.nih.gov/pubmed/22264684. Accessed 16 Jan 2014. (Elsevier B.V.).PubMedCrossRefGoogle Scholar
  110. 110.
    Moran RJ, Mallet N, Litvak V, Dolan RJ, Magill PJ, Friston KJ, et al. Alterations in brain connectivity underlying Beta oscillations in parkinsonism. PLoS Comput Biol. 2011;7(8):e1002124. doi: 10.1371/journal.pcbi.1002124. Epub 11 Aug 2011.PubMedCentralPubMedCrossRefGoogle Scholar
  111. 111.
    Marreiros AC, Cagnan H, Moran RJ, Friston KJ, Brown P. Basal ganglia-cortical interactions in parkinsonian patients. Neuroimage. 2012;66C:301–10. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3573233&tool=pmcentrez&rendertype=abstract. Accessed 16 Jan 2014.Google Scholar
  112. 112.
    Herz DM, Florin E, Christensen MS, Reck C, Barbe MT, Tscheuschler MK, et al. Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in parkinson's disease. Cereb Cortex. 2014;24(11):2873–83. doi: 10.1093/cercor/bht140. Epub 2 Jun 2013.Google Scholar
  113. 113.
    Herz DM, Siebner HR, Hulme OJ, Florin E, Christensen MS, Timmermann L. Levodopa reinstates connectivity from prefrontal to premotor cortex during externally paced movement in Parkinson’s disease. Neuroimage. 2014;90:15–23. doi: 10.1016/j.neuroimage.2013.11.023. Epub 22 Nov 2013.Google Scholar
  114. 114.
    Herz DM, Christensen MS, Reck C, Florin E, Barbe MT, Stahlhut C, et al. Task-specific modulation of effective connectivity during two simple unimanual motor tasks: a 122-channel EEG study. Neuroimage. 2012;59(4):3187–93. http://www.ncbi.nlm.nih.gov/pubmed/22146753. Accessed 19 Jan 2014. (Elsevier Inc).PubMedCrossRefGoogle Scholar
  115. 115.
    Boly M, Garrido MI, Gosseries O, Bruno M-A, Boveroux P, Schnakers C, et al. Preserved feedforward but impaired top-down processes in the vegetative state. Science. 2011;332:858–62.PubMedCrossRefGoogle Scholar
  116. 116.
    Boly M, Moran R, Murphy M, Boveroux P, Bruno M-A, Noirhomme Q, et al. Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness. J Neurosci. 2012;32(20):7082–90. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3366913&tool=pmcentrez&rendertype=abstract. Accessed 16 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  117. 117.
    Moran RJ, Jung F, Kumagai T, Endepols H, Graf R, Dolan RJ, et al. Dynamic causal models and physiological inference: a validation study using isoflurane anaesthesia in rodents. PLoS One. 2011;6(8):e22790. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3149050&tool=pmcentrez&rendertype=abstract. Accessed 19 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  118. 118.
    Muthukumaraswamy SD, Carhart-Harris RL, Moran RJ, Brookes MJ, Williams TM, Errtizoe D, et al. Broadband cortical desynchronization underlies the human psychedelic state. J Neurosci. 2013;33(38):15171–83. http://www.ncbi.nlm.nih.gov/pubmed/24048847. Accessed 14 Jan 2014.PubMedCrossRefGoogle Scholar
  119. 119.
    Schmidt A, Diaconescu AO, Kometer M, Friston KJ, Stephan KE, Vollenweider FX. Modeling ketamine effects on synaptic plasticity during the mismatch negativity. Cereb Cortex. 2013;23(10):2394–406. http://www.ncbi.nlm.nih.gov/pubmed/22875863. Accessed 19 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  120. 120.
    David O, Guillemain I, Saillet S, Reyt S, Deransart C, Segebarth C, et al. Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 2008;6(12):2683–97. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2605917&tool=pmcentrez&rendertype=abstract. Accessed 11 Jan 2014.PubMedCrossRefGoogle Scholar
  121. 121.
    Hamandi K, Powell HWR, Laufs H, Symms MR, Barker GJ, Parker GJM, et al. Combined EEG-fMRI and tractography to visualise propagation of epileptic activity. J Neurol Neurosurg Psychiatry. 2008;79(5):594–7. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2571962&tool=pmcentrez&rendertype=abstract. Accessed 10 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  122. 122.
    Vaudano AE, Laufs H, Kiebel SJ, Carmichael DW, Hamandi K, Guye M, et al. Causal hierarchy within the thalamo-cortical network in spike and wave discharges. PLoS One. 2009;4(8):e6475. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2715100&tool=pmcentrez&rendertype=abstract. Accessed 16 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  123. 123.
    Airaksinen AM, Niskanen J-P, Chamberlain R, Huttunen JK, Nissinen J, Garwood M, et al. Simultaneous fMRI and local field potential measurements during epileptic seizures in medetomidine-sedated rats using raser pulse sequence. Magn Reson Med. 2010;64(4):1191–9. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2946452&tool=pmcentrez&rendertype=abstract. Accessed 14 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  124. 124.
    Airaksinen AM, Hekmatyar SK, Jerome N, Niskanen J-P, Huttunen JK, Pitkänen A, et al. Simultaneous BOLD fMRI and local field potential measurements during kainic acid-induced seizures. Epilepsia. 2012;53(7):1245–53. http://www.ncbi.nlm.nih.gov/pubmed/22690801. Accessed 14 Jan 2014.PubMedCrossRefGoogle Scholar
  125. 125.
    Murta T, Leal A, Garrido MI, Figueiredo P. Dynamic causal modelling of epileptic seizure propagation pathways: a combined EEG-fMRI study. Neuroimage. 2012;62(3):1634–42. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3778869&tool=pmcentrez&rendertype=abstract. Accessed 16 Jan 2014. (Elsevier Inc.).PubMedCentralPubMedCrossRefGoogle Scholar
  126. 126.
    Vaudano AE, Carmichael DW, Salek-Haddadi A, Rampp S, Stefan H, Lemieux L, et al. Networks involved in seizure initiation. A reading epilepsy case studied with EEG-fMRI and MEG. Neurology. 2012;79(3):249–53. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3398433&tool=pmcentrez&rendertype=abstract.PubMedCentralPubMedCrossRefGoogle Scholar
  127. 127.
    Vaudano AE, Avanzini P, Tassi L, Ruggieri A, Cantalupo G, Benuzzi F, et al. Causality within the epileptic network: an EEG-fMRI study validated by intracranial EEG. Front Neurol. 2013;4(November):185. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3827676&tool=pmcentrez&rendertype=abstract. Accessed 17 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  128. 128.
    Campo P, Garrido MI, Moran RJ, García-Morales I, Poch C, Toledano R, et al. Network reconfiguration and working memory impairment in mesial temporal lobe epilepsy. Neuroimage. 2013;72:48–54. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3610031&tool=pmcentrez&rendertype=abstract. Accessed 9 Jan 2014. (Elsevier Inc.).PubMedCentralPubMedCrossRefGoogle Scholar
  129. 129.
    Albouy P, Mattout J, Bouet R, Maby E, Sanchez G, Aguera P-E, et al. Impaired pitch perception and memory in congenital amusia: the deficit starts in the auditory cortex. Brain. 2013;136(Pt 5):1639–61. http://www.ncbi.nlm.nih.gov/pubmed/23616587. Accessed 15 Jan 2014.PubMedCrossRefGoogle Scholar
  130. 130.
    Babajani-Feremi A, Gumenyuk V, Roth T, Drake CL, Soltanian-Zadeh H. Connectivity analysis of novelty process in habitual short sleepers. Neuroimage. 2012;63(3):1001–10. http://www.ncbi.nlm.nih.gov/pubmed/22906789. Accessed 13 Jan 2014. (Elsevier Inc.).PubMedCrossRefGoogle Scholar
  131. 131.
    Campo P, Poch C, Toledano R, Igoa JM, Belinchón M, García-Morales I, et al. Anterobasal temporal lobe lesions alter recurrent functional connectivity within the ventral pathway during naming. J Neurosci. 2013;33(31):12679–88. http://www.ncbi.nlm.nih.gov/pubmed/23904604. Accessed 19 Jan 2014.PubMedCrossRefGoogle Scholar
  132. 132.
    Hughes LE, Ghosh BCP, Rowe JB. Reorganisation of brain networks in frontotemporal dementia and progressive supranuclear palsy. NeuroImage Clin. 2013;2:459–68. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3708296&tool=pmcentrez&rendertype=abstract. Accessed 14 Jan 2014. (The Authors).PubMedCentralPubMedCrossRefGoogle Scholar
  133. 133.
    Kumar S, Sedley W, Nourski KV, Kawasaki H, Oya H, Patterson RD, et al. Predictive coding and pitch processing in the auditory cortex. J Cogn Neurosci. 2011;23(10):3084–94. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3821983&tool=pmcentrez&rendertype=abstract.PubMedCrossRefGoogle Scholar
  134. 134.
    Silchenko AN, Adamchic I, Hauptmann C, Tass PA. Impact of acoustic coordinated reset neuromodulation on effective connectivity in a neural network of phantom sound. Neuroimage. 2013;77:133–47. http://www.ncbi.nlm.nih.gov/pubmed/23528923. Accessed 14 Jan 2014. (Elsevier Inc.).PubMedCrossRefGoogle Scholar
  135. 135.
    Teki S, Barnes GR, Penny WD, Iverson P, Woodhead ZVJ, Griffiths TD, et al. The right hemisphere supports but does not replace left hemisphere auditory function in patients with persisting aphasia. Brain. 2013;136(Pt 6):1901–12. http://www.ncbi.nlm.nih.gov/pubmed/23715097. Accessed 15 Jan 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  136. 136.
    Pinotsis DA, Moran RJ, Friston KJ. Dynamic causal modeling with neural fields. Neuroimage. 2012 Jan 16;59(2):1261–74.Google Scholar
  137. 137.
    Smith Y, Bevan MD, Shink E, Bolam JP. Microcircuitry of the direct and indirect pathways of the basal ganglia. Neuroscience. 1998;86(2):353–87. http://www.ncbi.nlm.nih.gov/pubmed/9881853. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  138. 138.
    Gradinaru V, Mogri M, Thompson KR, Henderson JM, Deisseroth K. Optical deconstruction of parkinsonian neural circuitry. Science. 2009;324(5925):354–9. http://www.ncbi.nlm.nih.gov/pubmed/19299587. Accessed 21 March 2014.PubMedCrossRefGoogle Scholar
  139. 139.
    Magill PJ, Bolam JP, Bevan MD. Dopamine regulates the impact of the cerebral cortex on the subthalamic nucleus-globus pallidus network. Neuroscience. 2001;106(2):313–30. http://www.ncbi.nlm.nih.gov/pubmed/11566503. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  140. 140.
    Cruz A V, Mallet N, Magill PJ, Brown P, Averbeck BB. Effects of dopamine depletion on information flow between the subthalamic nucleus and external globus pallidus. J Neurophysiol. 2011;106(4):2012–23. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3191831&tool=pmcentrez&rendertype=abstract. Accessed 19 March 2014.PubMedCentralPubMedCrossRefGoogle Scholar
  141. 141.
    Holgado AJN, Terry JR, Bogacz R. Conditions for the generation of beta oscillations in the subthalamic nucleus-globus pallidus network. J Neurosci. 2010;30(37):12340–52. http://www.ncbi.nlm.nih.gov/pubmed/20844130. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  142. 142.
    Jenkinson N, Brown P. New insights into the relationship between dopamine, beta oscillations and motor function. Trends Neurosci. 2011;34(12):611–8. http://www.ncbi.nlm.nih.gov/pubmed/22018805. Accessed 22 March 2014.PubMedCrossRefGoogle Scholar
  143. 143.
    Chen CC, Lin WY, Chan HL, Hsu YT, Tu PH, Lee ST, et al. Stimulation of the subthalamic region at 20 Hz slows the development of grip force in Parkinson’s disease. Exp Neurol. 2011;231(1):91–6. http://www.ncbi.nlm.nih.gov/pubmed/21683700. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  144. 144.
    Eusebio A, Chen CC, Lu CS, Lee ST, Tsai CH, Limousin P, et al. Effects of low-frequency stimulation of the subthalamic nucleus on movement in Parkinson's disease. Exp Neurol. 2008;209(1):125–30. Epub 18 Sep 2007.PubMedCentralPubMedCrossRefGoogle Scholar
  145. 145.
    Baudrexel S, Witte T, Seifried C, von Wegner F, Beissner F, Klein JC, et al. Resting state fMRI reveals increased subthalamic nucleus-motor cortex connectivity in parkinson’s disease. Neuroimage. 2011;55(4):1728–38. http://www.ncbi.nlm.nih.gov/pubmed/21255661. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  146. 146.
    Dejean C, Gross CE, Bioulac B, Boraud T. Dynamic changes in the cortex-basal ganglia network after dopamine depletion in the rat. J Neurophysiol. 2008;100(1):385–96. http://www.ncbi.nlm.nih.gov/pubmed/18497362. Accessed 26 March 2014.PubMedCrossRefGoogle Scholar
  147. 147.
    Bergman H, Wichmann T, DeLong MR. Reversal of experimental parkinsonism by lesions of the subthalamic nucleus. Science. 1990;249(4975):1436–8. http://www.ncbi.nlm.nih.gov/pubmed/2402638.PubMedCrossRefGoogle Scholar
  148. 148.
    Kravitz AV, Freeze BS, Parker PRL, Kay K, Thwin MT, Deisseroth K, et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature. 2010;466(7306):622–6. doi: 10.1038/nature09159. Epub 7 Jul 2010.PubMedCentralPubMedCrossRefGoogle Scholar
  149. 149.
    Chen CC, Pogosyan A, Zrinzo LU, Tisch S, Limousin P, Ashkan K, et al. Intra-operative recordings of local field potentials can help localize the subthalamic nucleus in Parkinson’s disease surgery. Exp Neurol. 2006;198(1):214–21. http://www.ncbi.nlm.nih.gov/pubmed/16403500. Accessed 30 March 2014.PubMedCrossRefGoogle Scholar
  150. 150.
    Tachibana Y, Iwamuro H, Kita H, Takada M, Nambu A. Subthalamo-pallidal interactions underlying parkinsonian neuronal oscillations in the primate basal ganglia. Eur J Neurosci. 2011;34(9):1470–84. http://www.ncbi.nlm.nih.gov/pubmed/22034978. Accessed 19 March 2014.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Max Planck Institute for Biological CyberneticsTübingenGermany
  2. 2.Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUK
  3. 3.The Wellcome Trust Centre for NeuroimagingUniversity College LondonQueen SquareUK

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