VB_FCtemplateICA: VB_FCtemplateICA

In templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

VB_FCtemplateICA

Description

VB Algorithm for FC Template ICA Model

Usage

VB_FCtemplateICA(
  template_mean,
  template_var,
  template_FC,
  method_FC = c("VB1", "VB2"),
  nsamp_u = 10000,
  CI_FC = 0.95,
  return_FC_samp = FALSE,
  prior_params = c(0.001, 0.001),
  BOLD,
  TR = NULL,
  A0,
  S0,
  S0_var,
  maxiter = 100,
  miniter = 3,
  epsilon = 0.001,
  usePar = TRUE,
  PW = FALSE,
  verbose = FALSE
)

Arguments

template_mean	(V \times Q matrix) mean maps for each IC in the template, where Q is the number of ICs, and V=nvox is the number of data locations.
template_var	(V \times Q matrix) between-subject variance maps for each IC in the template.
template_FC	(list) Parameters of functional connectivity template.
method_FC	Variational Bayes (VB) method for FC template ICA model: "VB1" (default) uses a conjugate Inverse-Wishart prior for the cor(A); "VB2" draws samples from p(cor(A)) to emulate the population distribution using a combination of Cholesky, SVD, and random pivoting.
nsamp_u	For VB1, the number of samples to generate from u ~ Gamma, where A is Gaussian conditional on u. Default: 10000.
CI_FC	Level of posterior credible interval to construct for each FC element. Default: 0.95.
return_FC_samp	Should the FC samples (QxQxK) be returned, where K is the number of posterior samples generated? May be a large object. For VB1, K is equal to nsamp_u. For VB2, K is equal to the number of samples from p(G) contained in template_FC.
prior_params	Alpha and beta parameters of IG prior on \tau^2 (error variance). Default: 0.001 for both.
BOLD	(V \times T matrix) preprocessed fMRI data.
A0, S0, S0_var	Initial guesses at latent variables: A (TxQ mixing matrix), S (QxV matrix of spatial ICs), and variance matrix S0_var.
maxiter	Maximum number of VB iterations. Default: 100.
miniter	Minimum number of VB iterations. Default: 3.
epsilon	Smallest proportion change in parameter estimates between iterations. Default: 0.001.
usePar	Parallelize the computation? Default: FALSE. Can be the number of cores to use or TRUE, which will use the number available minus two.
PW	Prewhiten to account for residual autocorrelation?
verbose	If TRUE, display progress of algorithm. Default: FALSE.

Value

A list of computed values, including the final parameter estimates.

templateICAr documentation built on Oct. 14, 2024, 5:08 p.m.