DCM, Conductance Based Models and Clinical Applications

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


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.


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



Bayes factor


Basal ganglia nuclei


Bayesian model selection


Dynamic Causal Modelling






Event-Related Potential


Functional Magnetic Resonance Imaging




General linear model


Globus Pallidus externa


Globus Pallidus interna


Jansen and Rit




Local Field Potential




Mean field model


Method of moments


Mismatch Negativity


Magnetic Resonance Imaging


Neural mass model


Neural field model


Ordinary differential equation


Parkinson’s disease


Stochastic differential equation


Somatosensory evoked potential


Statistical parametric mapping


Steady state responses


Subthalamic nucleus



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.


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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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