Dynamic causal modeling: Difference between revisions

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==== EEG / MEG / LFP ====

EEG and MEG data support the estimation of more biologically detailed neural models than fMRI, as their higher temporal resolution provide access to richer neural dynamics. The models can be classed into phenomenological models, which focus on reproducing particular data features, and physiological models, which recapitulate neural circuity. The physiological models can be further subdivided into two classes - convolution models, which convolve pre-synaptic input by a synaptic kernel function, and [http://www.scholarpedia.org/article/Conductance-based_models conductance-based models], which derive from the equivalent circuit representation of the cell membrane developed by Hodgkin and Huxley in the 1950s<ref name=":5">{{Cite journal|last=Hodgkin|first=A. L.|last2=Huxley|first2=A. F.|date=1952-04-28|title=The components of membrane conductance in the giant axon ofLoligo|url=http://dx.doi.org/10.1113/jphysiol.1952.sp004718|journal=The Journal of Physiology|volume=116|issue=4|pages=473–496|doi=10.1113/jphysiol.1952.sp004718|issn=0022-3751}}</ref> and convolution models, which convolve pre-synaptic input by a synaptic kernel function, deriving from work by [[Wilson–Cowan model|Wilson & Cowan]]<ref>{{Cite journal|last=Wilson|first=H. R.|last2=Cowan|first2=J. D.|date=1973-09|title=A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue|url=http://dx.doi.org/10.1007/bf00288786|journal=Kybernetik|volume=13|issue=2|pages=55–80|doi=10.1007/bf00288786|issn=0340-1200}}</ref> and Freeman <ref>{{Cite journal|date=1975|title=Mass Action in the Nervous System|url=http://dx.doi.org/10.1016/c2009-0-03145-6|doi=10.1016/c2009-0-03145-6}}</ref> in the 1970s.

* Physiological models:

** Convolution models:

*** DCM for evoked responses (DCM for ERP) <ref>{{Cite journal|last=David|first=Olivier|last2=Friston|first2=Karl J.|date=2003-11|title=A neural mass model for MEG/EEG:|url=http://dx.doi.org/10.1016/j.neuroimage.2003.07.015|journal=NeuroImage|volume=20|issue=3|pages=1743–1755|doi=10.1016/j.neuroimage.2003.07.015|issn=1053-8119}}</ref><ref>{{Citation|last=Kiebel|first=Stefan J.|title=Dynamic Causal Modeling for Evoked Responses|date=2009-07-31|url=http://dx.doi.org/10.7551/mitpress/9780262013086.003.0006|work=Brain Signal Analysis|pages=141–170|publisher=The MIT Press|isbn=9780262013086|last2=Garrido|first2=Marta I.|last3=Friston|first3=Karl J.}}</ref>. This is a biologically plausible neural mass model, extending earlier work by Jansen and Rit<ref>{{Cite journal|last=Jansen|first=Ben H.|last2=Rit|first2=Vincent G.|date=1995-09-01|title=Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns|url=http://dx.doi.org/10.1007/s004220050191|journal=Biological Cybernetics|volume=73|issue=4|pages=357–366|doi=10.1007/s004220050191|issn=0340-1200}}</ref>. It emulates the activity of a cortical area using three neuronal sub-populations, each of which rests on two operators. The first transforms the pre-synaptic firing rate into a Post-Synaptic Potential (PSP), by [[Convolution|convolving]] a synaptic response function (kernel) by the pre-synaptic input. The second operator, a [[Sigmoid function|sigmoid]] function, transforms the membrane potential into a firing rate of action potentials.

*** DCM for LFP (Local Field Potentials) <ref>{{Cite journal|last=Moran|first=R.J.|last2=Kiebel|first2=S.J.|last3=Stephan|first3=K.E.|last4=Reilly|first4=R.B.|last5=Daunizeau|first5=J.|last6=Friston|first6=K.J.|date=2007-09|title=A neural mass model of spectral responses in electrophysiology|url=http://dx.doi.org/10.1016/j.neuroimage.2007.05.032|journal=NeuroImage|volume=37|issue=3|pages=706–720|doi=10.1016/j.neuroimage.2007.05.032|issn=1053-8119}}</ref>. Extends DCM for ERP by added the effects of specific ion channels on spike generation.

*** Canonical Microcircuit (CMC) <ref>{{Cite journal|last=Bastos|first=Andre M.|last2=Usrey|first2=W. Martin|last3=Adams|first3=Rick A.|last4=Mangun|first4=George R.|last5=Fries|first5=Pascal|last6=Friston|first6=Karl J.|date=2012-11|title=Canonical Microcircuits for Predictive Coding|url=http://dx.doi.org/10.1016/j.neuron.2012.10.038|journal=Neuron|volume=76|issue=4|pages=695–711|doi=10.1016/j.neuron.2012.10.038|issn=0896-6273}}</ref>. IntroducedUsed to address hypotheses about ascending and descending signals in the brain, which are thought to underpin [[predictive coding]],. theTo whichenable splitthis it splits the pyramidal cell population in DCM for ERP into deep and superficial populations.

** Conductance models:

***Neural Mass Model (NMM) and Mean-field model (MFM)<ref>{{Cite journal|last=Marreiros|first=André C.|last2=Daunizeau|first2=Jean|last3=Kiebel|first3=Stefan J.|last4=Friston|first4=Karl J.|date=2008-08|title=Population dynamics: Variance and the sigmoid activation function|url=http://dx.doi.org/10.1016/j.neuroimage.2008.04.239|journal=NeuroImage|volume=42|issue=1|pages=147–157|doi=10.1016/j.neuroimage.2008.04.239|issn=1053-8119}}</ref><ref>{{Cite journal|last=Marreiros|first=André C.|last2=Kiebel|first2=Stefan J.|last3=Daunizeau|first3=Jean|last4=Harrison|first4=Lee M.|last5=Friston|first5=Karl J.|date=2009-02|title=Population dynamics under the Laplace assumption|url=http://dx.doi.org/10.1016/j.neuroimage.2008.10.008|journal=NeuroImage|volume=44|issue=3|pages=701–714|doi=10.1016/j.neuroimage.2008.10.008|issn=1053-8119}}</ref>. These models have the same arrangement of neural populations as DCM for ERP, above, but are based on the Morris-Lecar model of the barnacle muscle fibre, which in turn derives from the Hodgin and Huxley model of the giant squid axon<ref name=":5" />. It enables inference about ligand-gated excitatory (Na+) and inhibitory (Cl-) ion flow, mediated through fast glutamatergic and GABAergic receptors. Whereas DCM for fMRI and the convolution models represent the activity of a neural population by a single number - its mean activity - the conductance models include the full density (probability distribution) of activity across the population. The 'mean-field assumption' used in the MFM version of the model means that the density of one population's activity depends only on the mean of other neural populations, and not the full probability density.

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* Phenomonological

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* Phenomonological models:

== Model estimation ==