VB_FCBrainMap: VB_FCBrainMap

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

VB_FCBrainMap

Description

VB Algorithm for FC Bayesian brain map

Usage

VB_FCBrainMap(
  prior_mean,
  prior_var,
  prior_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 = FALSE,
  PW = FALSE,
  seed = 1234,
  verbose = FALSE
)

Arguments

prior_mean	(V \times Q matrix) mean maps for each network in the prior, where Q is the number of networks, and V=nvox is the number of data locations.
prior_var	(V \times Q matrix) between-subject variance maps for each network in the prior.
prior_FC	(list) Parameters of functional connectivity prior.
method_FC	Variational Bayes (VB) method for FC Bayesian brain mapping: "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 prior_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 networks), 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? Default: FALSE.
seed	(Only applicable if PW) Seed to use for computing temporal effective sample size (ESS) to correct sums over t. If NULL, do not change the seed. Default: 1234.
verbose	If TRUE, display progress of algorithm. Default: FALSE.

Value

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

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