EM_templateICA {templateICAr}

EM Algorithms for Template ICA Models

Description

EM Algorithms for Template ICA Models

Usage

EM_templateICA.spatial(
  template_mean,
  template_var,
  meshes,
  BOLD,
  theta0,
  C_diag,
  maxiter = 100,
  usePar = FALSE,
  epsilon = 0.01,
  verbose = FALSE
)

EM_templateICA.independent(
  template_mean,
  template_var,
  BOLD,
  theta0,
  C_diag,
  maxiter = 100,
  epsilon = 0.01,
  usePar = FALSE,
  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

meshes

NULL for spatial independence model, otherwise a list of objects of class "templateICA_mesh" containing the triangular mesh (see make_mesh) for each brain structure.

BOLD

(V \times Q matrix) dimension-reduced fMRI data

theta0

(list) initial guess at parameter values: A (QxQ mixing matrix), nu0_sq (residual variance from first level) and (for spatial model only) kappa (SPDE smoothness parameter for each IC map)

C_diag

(Qx1) diagonal elements of matrix proportional to residual variance. of the spatial template ICA model, which assumes that all IC's have the same smoothness parameter, \kappa

maxiter

Maximum number of EM iterations. Default: 100.

usePar

Parallelize the computation over voxels? Default: FALSE. Can be the number of cores to use or TRUE, which will use the number on the PC minus two. Not implemented yet for spatial template ICA.

epsilon

Smallest proportion change between iterations. Default: 0.01.

verbose

If TRUE, display progress of algorithm. Default: FALSE.

Details

EM_templateICA.spatial implements the expectation-maximization (EM) algorithm described in Mejia et al. (2019+) for estimating the subject-level ICs and unknown parameters in the template ICA model with spatial priors on subject effects.

In both models, if original fMRI timeseries has covariance \sigma^2 I_T, the prewhitened timeseries achieved by premultiplying by (QxT) matrix H from PCA has diagonal covariance \sigma^2HH', so C_diag is diag(HH').

Value

A list: theta (list of final parameter estimates), subICmean (estimates of subject-level ICs), subICvar (variance of subject-level ICs, for non-spatial model) or subjICcov (covariance matrix of subject-level ICs, for spatial model – note that only diagonal and values for neighbors are computed), and success (flag indicating convergence (TRUE) or not (FALSE))