id_activations: Identify task activations

Description

Identify areas of activation for each task from the result of BayesGLM or BayesGLM_cifti.

Usage

id_activations(
  model_obj,
  tasks = NULL,
  sessions = NULL,
  method = c("Bayesian", "classical"),
  alpha = 0.05,
  gamma = NULL,
  correction = c("FWER", "FDR", "none"),
  verbose = 1
)

Value

An "act_BayesGLM" or "act_BayesGLM_cifti" object, a list which indicates the activated locations along with related information.

Arguments

model_obj

Result of BayesGLM or BayesGLM_cifti model call, of class "BayesGLM" or "BayesGLM_cifti".

tasks

The task(s) to identify activations for. Give either the name(s) as a character vector, or the numerical indices. If NULL (default), analyze all tasks.

sessions

The session(s) to identify activations for. Give either the name(s) as a character vector, or the numerical indices. If NULL (default), analyze the first session.

Currently, if multiple sessions are provided, activations are identified separately for each session. (Information is not combined between the different sessions.)

method

"Bayesian" (default) or "classical". If model_obj does not have Bayesian results because Bayes was set to FALSE, only the "classical" method can be used.

alpha

Significance level. Default: 0.05.

gamma

Activation threshold, for example 1 for 1\ change if scale_BOLD=="mean" during model estimation. Setting a gamma is required for the Bayesian method; NULL (default) will use a gamma of zero for the classical method.

correction

For the classical method only: Type of multiple comparisons correction: "FWER" (Bonferroni correction, the default), "FDR" (Benjamini Hochberg), or "none".

verbose

Should updates be printed? Use 1 (default) for occasional updates, 2 for occasional updates as well as running INLA in verbose mode (if applicable), or 0 for no updates.