EM_FCtemplateICA {templateICAr}

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))