Construct fMRI DCM priors

Source: R/fmri-model.R

Builds the prior expectations pE and prior covariance pC for a deterministic fMRI DCM. Mirrors SPM25's dcm_fmri_priors.

dcm_fmri_priors(A, B, C, D, options = list())

Arguments

A

Connectivity adjacency matrix.

B

Modulatory adjacency array.

C

Driving-input adjacency matrix.

D

Non-linear adjacency array.

options

List of model options (stochastic, induced, two_state, backwards, precision, decay).

Value

List with pE (prior expectation), x (initial state template), and pC (prior covariance).

Model variants

Only the deterministic, single-state model is supported end-to-end in this release. Branches for the two-state (options$two_state) and spectral (options$induced) variants exist but are experimental and are not wired through dcm_estimate, which rejects them. They are planned for a future update.

See also

dcm_estimate, which builds these priors for you.

Examples

# Priors for the bundled three-region (deterministic) model
data(toy_dcm)
pr <- dcm_fmri_priors(toy_dcm$a, toy_dcm$b, toy_dcm$c,
                      D = NULL, options = toy_dcm$options)
names(pr)
#> [1] "pE" "x"  "pC"
pr$pE$A          # prior expectation of the endogenous connections
#>           [,1]      [,2]      [,3]
#> [1,] 0.0078125 0.0078125 0.0078125
#> [2,] 0.0078125 0.0078125 0.0078125
#> [3,] 0.0078125 0.0078125 0.0078125
dim(pr$x)        # 3 regions x 5 hemodynamic states
#> [1] 3 5