SPDE_from_voxel

internal

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

SPDE_from_voxel function

<dl><dt>spatial</dt>
<dd>See <code>BayesGLM</code>.</dd>
<dt>qc_mask</dt>
<dd>QC mask.</dd>
<dt>logkappa, logtau</dt>
<dd>vector of min, max and initial value for prior on log kappa and log tau. Min and max are extreme quantiles, not hard constraints.</dd></dl>

Arguments

SPDE from voxel model — SPDE_from_voxel

<dl>

<dt>spatial</dt>
<dd>See <code>BayesGLM</code>.</dd>


<dt>qc_mask</dt>
<dd>QC mask.</dd>


<dt>logkappa, logtau</dt>
<dd>vector of min, max and initial value for prior on log kappa and log tau. Min and max are extreme quantiles, not hard constraints.</dd>

</dl>

SPDE from voxel model

SPDE_from_voxel: SPDE from voxel model

Description

Usage

Value

Arguments