DCM was developed for (and is applied principallyused to)estimatingestimate the coupling among brain regions and howthe thatchanges in coupling isdue influenced byto experimental changes (e.g., time or context). The basic idea is to construct reasonably realistic models of interacting brain regions. These models are then supplemented with a forward model of how the hidden states of each brain region (e.g., neuronal activity) causescause the measured responses. This enables the best model and its parameters (i.e., effective connectivity) to be identified from observed data. [[Bayesian model comparison]] is used to select the best model(s) inbased termson oftheir itstheir evidence, which can then be characterised in terms of itstheir parameters (e.g. connection strengths). This enables one to test hypotheses about how nodesbrain regions communicate; e.g., whether activityan inexperimental a given neuronal populationmanipulation modulates the coupling between otherneural populations, in a task-specific fashion.
== Procedure ==
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=== Experimental design ===
In functional neuroimaging, experiments are typically task-based or [[Resting state fMRI|resting state]]. In task-based designsexperiments, 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 encodedparameterized separately in DCM. ATo enable efficient estimation of driving and modulatory effects, a 2x2 [[Factorial experiment|factorial experimental design]] is often used - with one factor servingmodelled as the driving input and onethe other as athe modulatory input.
By contrast, resting state experiments have no experimental manipulations within the period of recording neuroimaging datarecording. Instead, the interest is in the endogenous fluctations in brain connectivity within a scan, or in the differences in connectivity between scans or subjects. DCM has been extended to enable modelling of endogenous fluctuations in absence of experimental input.
