vol2spde

Construct a triangular mesh from a 3D volumetric mask

Performs a spatial Bayesian general linear model (GLM) for task
functional magnetic resonance imaging (fMRI) data on the cortical surface.
Additional models include group analysis and inference to detect thresholded
areas of activation. Includes direct support for the 'CIFTI' neuroimaging
file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F.
Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D.
Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022)
<doi:10.1016/j.neuroimage.2022.118908>.

Amanda Mejia

BayesfMRI

Spatial Bayesian Methods for Task Functional MRI Studies

Damon Pham

David Bolin

Yu (Ryan) Yue

Daniel Spencer

Sarah Ryan

vol2spde function

<dl><dt>mask</dt>
<dd>An array of 0s and 1s representing a volumetric mask</dd>
<dt>res</dt>
<dd>The spatial resolution in each direction, in mm. For example, c(2,2,2) indicates 2mm isotropic voxels.</dd>
<dt>nbhd_order</dt>
<dd>For volumetric data, what order neighborhood around data
locations to keep? (0 = no neighbors, 1 = 1st-order neighbors, 2 = 1st- and
2nd-order neighbors, etc.). Smaller values will provide greater computational
efficiency at the cost of higher variance around the edge of the data.</dd>
<dt>buffer</dt>
<dd>For volumetric data, size of extra voxels layers around the
bounding box, in terms of voxels. Set to NULL for no buffer.</dd></dl>

Arguments

This function requires the <code>INLA</code> package, which is not a CRAN package.
See <a href="https://www.r-inla.org/download-install">https://www.r-inla.org/download-install</a> for easy installation instructions.

INLA Requirement

Construct a triangular mesh from a 3D volumetric mask — vol2spde

<dl>

<dt>mask</dt>
<dd>An array of 0s and 1s representing a volumetric mask</dd>


<dt>res</dt>
<dd>The spatial resolution in each direction, in mm. For example, c(2,2,2) indicates 2mm isotropic voxels.</dd>


<dt>nbhd_order</dt>
<dd>For volumetric data, what order neighborhood around data
locations to keep? (0 = no neighbors, 1 = 1st-order neighbors, 2 = 1st- and
2nd-order neighbors, etc.). Smaller values will provide greater computational
efficiency at the cost of higher variance around the edge of the data.</dd>


<dt>buffer</dt>
<dd>For volumetric data, size of extra voxels layers around the
bounding box, in terms of voxels. Set to NULL for no buffer.</dd>

</dl>

This function requires the <code>INLA</code> package, which is not a CRAN package.
See <a href='https://www.r-inla.org/download-install'>https://www.r-inla.org/download-install</a> for easy installation instructions.

vol2spde: Construct a triangular mesh from a 3D volumetric mask

Description

Usage

Value

Arguments

INLA Requirement