EM_FCtemplateICA: EM Algorithm for FC Template ICA Model

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

EM Algorithm for FC Template ICA Model

Description

EM Algorithm for FC Template ICA Model

Usage

EM_FCtemplateICA(
  template_mean,
  template_var,
  template_FC,
  prior_params = c(0.001, 0.001),
  BOLD,
  AS_0,
  maxiter = 100,
  epsilon = 0.01,
  verbose
)

Arguments

template_mean	(V \times Q matrix) mean maps for each IC in template, where Q is the number of ICs, V=nvox is the number of data locations.
template_var	(V \times Q matrix) between-subject variance maps for each IC in template
template_FC	(list) Parameters of functional connectivity template
prior_params	Alpha and beta parameters of IG prior on tau^2 (error variance)
BOLD	(V \times T matrix) preprocessed fMRI data
AS_0	(list) initial guess at latent variables: A (TxQ mixing matrix), and S (QxV matrix of spatial ICs)
maxiter	Maximum number of EM iterations. Default: 100.
epsilon	Smallest proportion change in parameter estimates between iterations. Default: 0.01.
verbose	If TRUE, display progress of algorithm. Default: FALSE.

Details

EM_FCtemplateICA implements the expectation-maximization (EM) algorithm for the functional connectivity (FC) template ICA model

Value

A list: theta (list of final parameter estimates), subICmean (estimates of subject-level ICs), subICvar (variance of subject-level ICs), mixing_mean (estimates of subject-level mixing matrix), mixing_var (variance of subject-level mixing matrix), success (flag indicating convergence (TRUE) or not (FALSE))

templateICAr documentation built on Feb. 16, 2023, 8:14 p.m.