Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Dynamic Causal Modelling with Neural Population Models

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_57


Dynamic causal models with neural populations are state-space models that impose a specified dynamical systems form to the generation of neuroimaging and electrophysiological data. The forms of these models constitute neurobiologically motivated and identifiable parameterizations, where empirical observations offer conditional descriptions of parameter space following application of a Bayesian inversion scheme. They comprise separate generative processes at the neuronal level and at the observation level through a set of deterministic or stochastic differential equations and an observer functional, respectively.

Detailed Description

Dynamic causal models were first invented in 2003 for the purpose of estimating human brain connectivity and task-dependent functional integration using functional magnetic resonance imaging (fMRI) (Friston et al. 2003). The framework was extended for electrophysiological domains including noninvasive electroencephalography (EEG) and...

This is a preview of subscription content, log in to check access.


  1. Buxton R, Wong E, Frank L (1998) Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med 39:855–864PubMedGoogle Scholar
  2. David O, Friston KJ (2003) A neural mass model for MEG/EEG: coupling and neuronal dynamics. Neuroimage 20:1743–1755PubMedGoogle Scholar
  3. den Ouden HE, Daunizeau J, Roiser J, Friston KJ, Stephan KE (2010) Striatal prediction error modulates cortical coupling. J Neurosci 30:3210–3219Google Scholar
  4. Dima D, Roiser JP, Dietrich DE, Bonnemann C, Lanfermann H, Emrich HM, Dillo W (2009) Understanding why patients with schizophrenia do not perceive the hollow-mask illusion using dynamic causal modelling. Neuroimage 46:1180–1186PubMedGoogle Scholar
  5. Fleming SM, Thomas CL, Dolan RJ (2010) Overcoming status quo bias in the human brain. Proc Natl Acad Sci 107:6005–6009PubMedCentralPubMedGoogle Scholar
  6. Friston K, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19:1273–1302PubMedGoogle Scholar
  7. Garrido M, Kilner J, Kiebel S, Friston K (2007) Evoked brain responses are generated by feedback loops. Proc Natl Acad Sci 104:20961PubMedCentralPubMedGoogle Scholar
  8. Jansen B, Rit V (1995) Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol Cybern 73:357–366PubMedGoogle Scholar
  9. Kiebel S, David O, Friston K (2006) Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization. Neuroimage 30:1273–1284PubMedGoogle Scholar
  10. Marreiros AC, Cagnan H, Moran RJ, Friston KJ, Brown P (2012) Basal ganglia-cortical interactions in Parkinsonian patients. Neuroimage 66:301–310PubMedGoogle Scholar
  11. Moran RJ, Symmonds M, Stephan KE, Friston KJ, Dolan RJ (2011) An in vivo assay of synaptic function mediating human cognition. Curr Biol 21:1320–1325PubMedCentralPubMedGoogle Scholar
  12. Šmídl V, Quinn AP (2006) The variational Bayes method in signal processing. Springer, New YorkGoogle Scholar
  13. Stephan KE, Penny WD, Marshall JC, Fink GR, Friston KJ (2005) Investigating the functional role of callosal connections with dynamic causal models. Ann N Y Acad Sci 1064:16–36PubMedCentralPubMedGoogle Scholar
  14. Stephan K, Penny W, Moran R, den Ouden H, Daunizeau J, Friston K (2010) Ten simple rules for dynamic causal modeling. Neuroimage 49:3099–3109PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Virginia Tech Carilion Research InstituteRoanokeUSA