Dynamic causal models (DCM) of steady-state responses is a new methodology available for the analysis of M/EEG or intracranial data in the frequency domain. This new DCM follows previous DCM frameworks by offering a mechanistic description of how a distributed neuronal network produces observed data. The key difference lies in the type of data this model can explain, namely, stationary oscillatory dynamics. Frequency responses that occur without time-dependency during some experimental event are summarised in terms of their cross-spectral density.
 
These M/EEG or intracranial responses in the frequency domain form the data feature which is explained in terms of neuronal parameters by employing a Bayesian inversion of a coupled neural mass model. The parameterisation takes into account the types (inhibitory/excitatory) and direction of extrinsic (between source) cortical connections and also includes meaningful physiological parameters of within-source activity e.g., post-synaptic receptor density and time constants.
 
Under linearity and stationarity assumptions, the biophysical parameters of this model prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., M/EEG data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. Thus inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations can be made.

R. Moran, K.E. Stephan, T. Seidenbecher, H.-C. Pape, R. Dolan and K. Friston. Dynamic Causal Models of steady-state responses. NeuroImage. In Press.
 
These descriptions of the new features are taken from the SPM8 Release Notes
 
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