postgroupglmcoef: Obtain posterior group estimate of a 2D GLM coefficients map...

In BHMSMAfMRI: Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI (fMRI) Data

Obtain posterior group estimate of a 2D GLM coefficients map of a regressor

Description

postgroupglmcoef computes posterior group mean (or group median) of a 2D GLM coefficients map (e.g., corresponding to a single brain slice) of a regressor using the posterior means (or medians) of the corresponding wavelet coefficients from all subjects in the inverse discrete wavelet transform based on multi-subject or single subject analyses (see References).

Usage

postgroupglmcoef( n, grid, glmcoefstd, postmeanwaveletcoef, 
wave.family="DaubLeAsymm", filter.number=6, bc="periodic" )

Arguments

n	Number of subjects.
grid	The number of voxels in one row (or, one column) of the brain slice of interest. Must be a power of 2. The total number of voxels is grid^2. The maximum value of grid for this package is 512.
glmcoefstd	An array of dimension (n,grid,grid), containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels.
postmeanwaveletcoef	A matrix of size (n,grid^2-1), containing for each subject the posterior mean of the wavelet coefficients of all levels stacked together (by the increasing order of resolution level).
wave.family	The family of wavelets to use - "DaubExPhase" or "DaubLeAsymm". Default is "DaubLeAsymm".
filter.number	The number of vanishing moments of the wavelet. Default is 6.
bc	The boundary condition to use - "periodic" or "symmetric". Default is "periodic".

Details

The wavelet transformation and reconstruction are performed by using the functions imwd and imwr, respectively.

Value

A list containing the following.

groupcoef

A matrix of dimension (grid, grid), containing the posterior group coefficients obtained by BHMSMA methodology.

Author(s)

Nilotpal Sanyal, Marco Ferreira

Maintainer: Nilotpal Sanyal <[e-mail]>

References

Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.

See Also

readfmridata, glmcoef, postglmcoef, postwaveletcoef, substituteWaveletCoef, imwd, imwr

Examples

set.seed(1)
n <- 3
grid <- 8
glmcoefstd <- array(rnorm(n*grid*grid),
  dim=c(n,grid,grid))
postmeanwaveletcoef <- array(rnorm(n*(grid^2-1)),
  dim=c(n,grid^2-1))
post.groupcoef <- postgroupglmcoef(n,grid,glmcoefstd,
  postmeanwaveletcoef)
dim(post.groupcoef$groupcoef)
#[1] 8 8

BHMSMAfMRI documentation built on Oct. 2, 2022, 9:05 a.m.