Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis

  • Richard E. RoschEmail author
  • Gerald Cooray
  • Karl J. Friston
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 6)


Using electroencephalography (EEG) dynamic brain function can be measured and its abnormalities identified and described. However, inferring pathological mechanisms from EEG recordings is an ill-posed, inverse problem. Here we illustrate the use of neural mass model based dynamic causal modelling to address this inverse problem. Using Bayesian model inversion and model comparison, DCM allows evaluation of different hypotheses regarding pathomechanisms leading to dynamic brain dysfunction in NMDA receptor encephalitis.


Model Inversion Model Evidence Neural Mass Model Dynamic Causal Modelling NMDAR Antibody 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



RER is funded by a Wellcome Trust Clinical Research (106556/Z/14/Z). KJF is funded by a Wellcome Trust Principal Research Fellowship (088130/Z/09/Z).


  1. 1.
    Aburn, M.J. et al., 2012. Critical Fluctuations in Cortical Models Near Instability. Frontiers in Physiology, 3, p. 331.Google Scholar
  2. 2.
    Babajani-Feremi, A. & Soltanian-Zadeh, H., 2010. Multi-area neural mass modeling of EEG and MEG signals. NeuroImage, 52(3), pp. 793–811.Google Scholar
  3. 3.
    Bastos, A.M. et al., 2012. Canonical Microcircuits for Predictive Coding. Neuron, 76(4), pp. 695–711.Google Scholar
  4. 4.
    Buffalo, E.A. et al., 2011. Laminar differences in gamma and alpha coherence in the ventral stream. Proceedings of the National Academy of Sciences, 108(27), pp. 11262–11267.Google Scholar
  5. 5.
    Bullmore, E. & Sporns, O., 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), pp. 186–198.Google Scholar
  6. 6.
    Canolty, R.T. & Knight, R.T., 2010. The functional role of cross-frequency coupling. Trends in Cognitive Sciences, 14(11), pp. 506–515.Google Scholar
  7. 7.
    Carandini, M., 2012. From circuits to behavior: a bridge too far? Nature Neuroscience, 15(4), pp. 507–509.Google Scholar
  8. 8.
    Clark, B.A. & Cull-Candy, S.G., 2002. Activity-dependent recruitment of extrasynaptic NMDA receptor activation at an AMPA receptor-only synapse. Journal of Neuroscience, 22(11), pp. 4428–4436.Google Scholar
  9. 9.
    Cooray, G.K. et al., 2016. Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating. NeuroImage, 125, pp. 1142–1154.Google Scholar
  10. 10.
    Dalmau, J. et al., 2008. Anti-NMDA-receptor encephalitis: case series and analysis of the effects of antibodies. The Lancet Neurology, 7(12), pp. 1091–1098.Google Scholar
  11. 11.
    David, O. et al., 2006. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage, 30(4), pp. 1255–1272.Google Scholar
  12. 12.
    David, O. & Friston, K.J., 2003. A neural mass model for MEG/EEG: NeuroImage, 20(3), pp. 1743–1755.Google Scholar
  13. 13.
    Van Dellen, E. et al., 2012. MEG Network Differences between Low- and High-Grade Glioma Related to Epilepsy and Cognition D.R. Chialvo, ed. PLoS ONE, 7(11), p.e50122.Google Scholar
  14. 14.
    Do, C.B. & Batzoglou, S., 2008. What is the expectation maximization algorithm? Nature Biotechnology, 26(8), pp. 897–899.Google Scholar
  15. 15.
    Du, J., Vegh, V. & Reutens, D.C., 2012. The Laminar Cortex Model: A New Continuum Cortex Model Incorporating Laminar Architecture L. J. Graham, ed. PLoS Computational Biology, 8(10), p.e1002733.Google Scholar
  16. 16.
    Eadie, M.J. & Bladin, P.F., 2001. A Disease Once Sacred. A History of the Medical Understanding of Epilepsy 1st ed., New Barnet: John Libbey & Co Ltd.Google Scholar
  17. 17.
    Fisher, R.S. et al., 2014. ILAE Official Report: A practical clinical definition of epilepsy. Epilepsia, 55(4), pp. 475–482.Google Scholar
  18. 18.
    Florance, N.R. et al., 2009. Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in children and adolescents. Annals of Neurology, 66(1), pp. 11–18.Google Scholar
  19. 19.
    Freestone, D.R. et al., 2014. Estimation of effective connectivity via data-driven neural modeling. Frontiers in Neuroscience, 8, p. 383.Google Scholar
  20. 20.
    Friston, K., 2005. A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), pp. 815–836.Google Scholar
  21. 21.
    Friston, K. et al., 2008. Multiple sparse priors for the M/EEG inverse problem. NeuroImage, 39(3), pp. 1104–1120.Google Scholar
  22. 22.
    Friston, K. et al., 2007. Variational free energy and the Laplace approximation. NeuroImage, 34(1), pp. 220–234.Google Scholar
  23. 23.
    Friston, K., Zeidman, P. & Litvak, V., 2015. Empirical Bayes for DCM: A Group Inversion Scheme. Frontiers in Systems Neuroscience, 9(November), pp. 1–10.Google Scholar
  24. 24.
    Friston, K.J. et al., 2016. Bayesian model reduction and empirical Bayes for group (DCM) studies. NeuroImage, 128, pp. 413–431.Google Scholar
  25. 25.
    Gitiaux, C. et al., 2013. Early electro-clinical features may contribute to diagnosis of the anti-NMDA receptor encephalitis in children. Clinical Neurophysiology, 124(12), pp. 2354–2361.Google Scholar
  26. 26.
    Gollas, F. & Tetzlaff, R., 2005. Modeling brain electrical activity in epilepsy by reaction-diffusion cellular neural networks. In R. A. Carmona & G. Linan-Cembrano, eds. p. 219.Google Scholar
  27. 27.
    Goodfellow, M., Schindler, K. & Baier, G., 2012. Self-organised transients in a neural mass model of epileptogenic tissue dynamics. NeuroImage, 59(3), pp. 2644–2660.Google Scholar
  28. 28.
    Hansen, S.T. & Hansen, L.K., 2015. EEG source reconstruction performance as a function of skull conductance contrast. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 827–831.Google Scholar
  29. 29.
    Heitmann, S., Gong, P. & Breakspear, M., 2012. A computational role for bistability and traveling waves in motor cortex. Frontiers in Computational Neuroscience, 6, p. 67.Google Scholar
  30. 30.
    Helbig, I. et al., 2008. Navigating the channels and beyond: unravelling the genetics of the epilepsies. The Lancet Neurology, 7(3), pp. 231–245.Google Scholar
  31. 31.
    Hildebrand, M.S. et al., 2013. Recent advances in the molecular genetics of epilepsy. Journal of Medical Genetics, 50(5), pp. 271–279.Google Scholar
  32. 32.
    Hodgkin, A.L. & Huxley, A.F., 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), pp. 500–44.Google Scholar
  33. 33.
    Hughes, E.G. et al., 2010. Cellular and Synaptic Mechanisms of Anti-NMDA Receptor Encephalitis. Journal of Neuroscience, 30(17), pp. 5866–5875.Google Scholar
  34. 34.
    Jansen, B.H. & Rit, V.G., 1995. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics, 73(4), pp. 357–66.Google Scholar
  35. 35.
    Julier, S.J. & Uhlmann, J.K., 2004. Unscented Filtering and Nonlinear Estimation. Proceedings of the IEEE, 92(3), pp. 401–422.Google Scholar
  36. 36.
    Jung, R. & Berger, W., 1979. Hans Bergers Entdeckung des Elektrenkephalogramms und seine ersten Befunde 1924–1931. Archiv fuer Psychiatrie und Nervenkrankheiten, 227(4), pp. 279–300.Google Scholar
  37. 37.
    Kawato, M., Hayakawa, H. & Inui, T., 1993. A forward-inverse optics model of reciprocal connections between visual cortical areas. Network, 4(4), p. 4150422.Google Scholar
  38. 38.
    Koch, C., Rapp, M. & Segev, I., 1996. A brief history of time (constants). Cerebr Cortex, 6, pp. 93–101.Google Scholar
  39. 39.
    Lantz, G., Grouiller, F. & Spinelli, L., 2011. Localisation of Focal Epileptic Activity in Children Using High Density EEG Source Imaging. Epileptologie, 28, pp. 84–90.Google Scholar
  40. 40.
    Lesca, G. et al., 2013. GRIN2A mutations in acquired epileptic aphasia and related childhood focal epilepsies and encephalopathies with speech and language dysfunction. Nature Genetics, 45(9), pp. 1061–1066.Google Scholar
  41. 41.
    Litvak, V. et al., 2015. Empirical Bayes for Group (DCM) Studies: A Reproducibility Study. Frontiers in Human Neuroscience, 9(Dcm), pp. 1–12.Google Scholar
  42. 42.
    Di Lollo, V., 2012. The feature-binding problem is an ill-posed problem. Trends in Cognitive Sciences, 16(6), pp. 317–321.Google Scholar
  43. 43.
    Lopes da Silva, F.H., 2010. MEG: An Introduction to Methods, Oxford University Press.Google Scholar
  44. 44.
    Lopes da Silva, F.H. et al., 1974. Model of brain rhythmic activity. The alpha-rhythm of the thalamus. Kybernetik, 15(1), pp. 27–37.Google Scholar
  45. 45.
    Meijer, H.G.E. et al., 2015. Modeling Focal Epileptic Activity in the Wilson–Cowan Model with Depolarization Block. The Journal of Mathematical Neuroscience, 5(1), p. 7.Google Scholar
  46. 46.
    Miller, I.O. & Sotero de Menezes, M.A., 2014. SCN1A-Related Seizure Disorders. GeneReviews.Google Scholar
  47. 47.
    Moran, R., Pinotsis, D. a & Friston, K., 2013. Neural masses and fields in dynamic causal modeling. Frontiers in Computational Neuroscience, 7(May), p. 57.Google Scholar
  48. 48.
    Nevado-Holgado, A.J. et al., 2012. Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution. NeuroImage, 59(3), pp. 2374–2392.Google Scholar
  49. 49.
    Nosadini, M. et al., 2015. Longitudinal Electroencephalographic (EEG) Findings in Pediatric Anti-N-Methyl-D-Aspartate (Anti-NMDA) Receptor Encephalitis: The Padua Experience. Journal of Child Neurology, 30(2), pp. 238–245.Google Scholar
  50. 50.
    Pal, D. & Helbig, I., 2015. Commentary: Pathogenic EFHC1 mutations are tolerated in healthy individuals dependent on reported ancestry. Epilepsia, 56(2), pp. 195–196.Google Scholar
  51. 51.
    Panayiotopoulos, C., 2005. The Epilepsies 1st ed., Oxford: Bladon Medical Publishing.Google Scholar
  52. 52.
    Papadopoulou, M. et al., 2015. Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy. NeuroImage, 107, pp. 117–126.Google Scholar
  53. 53.
    Quintáns, B. et al., 2014. Medical genomics: The intricate path from genetic variant identification to clinical interpretation. Applied & Translational Genomics, 3(3), pp. 60–67.Google Scholar
  54. 54.
    Shirvany, Y. et al., 2012. Non-invasive EEG source localization using particle swarm optimization: A clinical experiment. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 6232–6235.Google Scholar
  55. 55.
    Sitnikova, E. et al., 2008. Granger causality: Cortico-thalamic interdependencies during absence seizures in WAG/Rij rats. Journal of Neuroscience Methods, 170(2), pp. 245–254.Google Scholar
  56. 56.
    Spillane, J., Kullmann, D.M. & Hanna, M.G., 2015. Genetic neurological channelopathies: molecular genetics and clinical phenotypes. Journal of Neurology, Neurosurgery & Psychiatry, p.jnnp–2015–311233.Google Scholar
  57. 57.
    Thomas, E.M. et al., 2008. Seizure detection in neonates: Improved classification through supervised adaptation. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 903–906.Google Scholar
  58. 58.
    Thomas, R.H. & Berkovic, S.F., 2014. The hidden genetics of epilepsy—a clinically important new paradigm. Nature Reviews Neurology, 10(5), pp. 283–292.Google Scholar
  59. 59.
    Tong, S. & Thakor, N.V., 2009. Quantitative EEG Analysis Methods and Clinical Applications, Artech House.Google Scholar
  60. 60.
    Wang, Y. et al., 2014. Dynamic Mechanisms of Neocortical Focal Seizure Onset B. Ermentrout, ed. PLoS Computational Biology, 10(8), p.e1003787.Google Scholar
  61. 61.
    Werner, G., 2007. Metastability, criticality and phase transitions in brain and its models. Biosystems, 90(2), pp. 496–508.Google Scholar
  62. 62.
    Wilson, H.R. & Cowan, J.D., 1972. Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical journal, 12(1), pp. 1–24.Google Scholar
  63. 63.
    Zschocke, S. & Hansen, H.-C., 2012. Klinische Elektroenzepalographie 3rd ed., Berlin, Heidelberg: Springer-Verlag.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Richard E. Rosch
    • 1
    • 2
    Email author
  • Gerald Cooray
    • 1
    • 3
  • Karl J. Friston
    • 1
  1. 1.Wellcome Trust Centre for NeuroimagingInstitute of Neurology, University College LondonLondonUK
  2. 2.Centre for Developmental Cognitive NeuroscienceInstitute of Child Health, University College LondonLondonUK
  3. 3.Department of Clinical NeurophysiologyKarolinska University HospitalStockholmSweden

Personalised recommendations