Dynamic causal modeling: Difference between revisions

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In functional neuroimaging, experiments are typically task-based or [[Resting state fMRI|resting state]]. In task-based experiments, brain responses are evoked by known deterministic inputs (experimentally controlled stimuli) that embody designed changes in sensory stimulation or cognitive set. These experimental or exogenous variables can change neural activity in one of two ways. First, they can elicit responses through direct influences on specific brain regions. This would include, for example, sensory evoked responses in the early visual cortex. The second class of inputs exerts their effects vicariously, through a modulation of the coupling among nodes, for example, the influence of attention on the processing of sensory information. These two types of input - driving and modulatory - are parameterized separately in DCM. To enable efficient estimation of driving and modulatory effects, a 2x2 [[Factorial experiment|factorial experimental design]] is often used - with one factor modelled as the driving input and the other as the modulatory input.

By contrast, restingResting state experiments have no experimental manipulations within the period of recordingthe neuroimaging recording. Instead, the interest is in the endogenous fluctationsfluctuations in brain connectivity withinduring athe scan are of interest, or in the differences in connectivity between scans or subjects. The DCM hasframework beenincludes extendedmodels toand enableprocedures modellingfor ofresting endogenousstate, fluctuationsdescribed in absence of experimental inputbelow..

=== PreprocessingData preparation ===

For fMRI analysis, summary timeseries are generated for each brain region of interest. For MEG or EEG analysis, the desired data features are selected - e.g. [[Evoked potential|evoked potentials]] or induced responses.

=== Model specification ===

Dynamic Causal Models (DCMs) are nonlinear state-space models in continuous time that model the dynamics of hidden states in the nodes of a probabilistic graphical model, where conditional dependencies are parameterisedparameterized in terms of directed effective connectivity. Unlike [[Bayesian network|Bayesian Networks]] DCMs can be cyclic, and unlike [[Structural equation modeling|Structural Equation modelling]] and [[Granger causality]], DCM does not depend on the theory of Martingales, i.e., it does not assume that random fluctuations' are serially uncorrelated. Various models have been developed for use with DCM and the experimenter selects their preferred model based on the types of hypothesis they wish to address and the type of data they have collected.

=== Estimation ===