Subject-level DCM summaries for a group analysis (NARPS)

Subject-level DCM posterior summaries for 48 subjects, used to demonstrate the robust and sparse group-level model rsdcm. The summaries are the inputs a group analysis needs: a posterior mean and covariance per subject.

narps_dcm

Format

A list with:

eta

48 x 22 matrix of subject-level posterior means (subjects in rows, parameters in columns).

Cp

Length-48 list of 22 x 22 posterior covariance matrices.

parameter_names

Length-22 character vector, e.g. "A(1,1)", "B(2,1,1)".

subject

Length-48 character vector of de-identified subject ids.

covariates

Data frame of between-subject covariates: group, gender, age.

regions

The four region labels, in order.

Source

Subject-level Dynamic Causal Modelling summaries computed by the package author from the openly shared NARPS dataset: Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., et al. (2020), "Variability in the analysis of a single neuroimaging dataset by many teams", Nature, 582(7810), 84-88, doi:10.1038/s41586-020-2314-9 . The build script that prepared the shipped object is in data-raw/make-narps-dcm.R.

Details

Each subject has 22 parameters: the 16 intrinsic connections of a four-region DCM (A) plus 6 task-modulatory connections (B), across the regions vmPFC, vStr (ventral striatum), amygdala, and anterior insula.

See also

rsdcm, which this dataset is the example input for.

Examples

data(narps_dcm)
dim(narps_dcm$eta)               # 48 subjects x 22 parameters
#> [1] 48 22
length(narps_dcm$Cp)             # one posterior covariance per subject
#> [1] 48
table(narps_dcm$covariates$group)
#> 
#> equalIndifference        equalRange 
#>                25                23