engagements: Engagements of (spatial) Bayesian brain mapping

In BayesBrainMap: Estimate Brain Networks and Connectivity with Population-Derived Priors

Engagements of (spatial) Bayesian brain mapping

Description

Identify areas of engagement in each network from the result of (spatial) Bayesian brain mapping.

Usage

engagements(
  bMap,
  u = NULL,
  z = NULL,
  alpha = 0.01,
  type = c(">", "abs >", "<", "!="),
  method_p = "BH",
  verbose = FALSE,
  which.nets = NULL,
  deviation = FALSE
)

Arguments

bMap	Fitted (spatial) Bayesian brain map from BrainMap.
u, z	Set a threshold value for engagement? A threshold value can be specified directly with u, or a z-score-like threshold in terms of standard deviations (the SD of values in the mean prior) can be specified with z. Only one type of threshold can be used. Default: NULL (do not use a threshold). Either argument can also be a vector to test multiple thresholds at once, as long as type is not "!=" (to ensure the engagement regions are successive subsets).
alpha	Significance level for hypothesis testing. Default: 0.01.
type	Type of region: ">" (default), "abs >", "<", or "!=". "abs >" tests for magnitude by taking the absolute value and then testing if they are greater than... .
method_p	If the input is a "bMap.[format]" model object, the type of multiple comparisons correction to use for p-values, or NULL for no correction. See help(p.adjust). Default: "BH" (Benjamini & Hochberg, i.e. the false discovery rate). Note that multiple comparisons will account for data locations, but not networks.
verbose	If TRUE, display progress of algorithm. Default: FALSE.
which.nets	Indices of networks for which to identify engagements. If NULL (default), use all networks.
deviation	If TRUE identify significant deviations from the prior mean, rather than significant areas of engagement. Default: FALSE.

Value

A list containing engagement maps for each network, the joint and marginal PPMs for each network, and the parameters used for computing engagement. If the input represented CIFTI- or NIFTI-format data, then the engagements maps will be formatted accordingly.

Use summary to obtain information about the engagements results. For CIFTI-format engagements, use plot to visualize the engagement maps.

Examples

## Not run: 
 engagements(bMap_result, alpha=.05, deviation=TRUE)

## End(Not run)

BayesBrainMap documentation built on Aug. 8, 2025, 7:25 p.m.