pw_smooth

Smooth AR coefficients and white noise variance

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

pw_smooth function

<dl><dt>spatial</dt>
<dd>See <code>fit_bayesglm</code> internal code.</dd>
<dt>AR</dt>
<dd>A Vxp matrix of estimated AR coefficients, where V is the number of vertices and p is the AR model order</dd>
<dt>var</dt>
<dd>A vector length V containing the white noise variance estimates from the AR model</dd>
<dt>FWHM</dt>
<dd>FWHM parameter for smoothing. Remember that
\(\sigma = \frac{FWHM}{2*sqrt(2*log(2)}\). Set to <code>0</code> or <code>NULL</code>
to not do any smoothing. Default: <code>5</code>.</dd></dl>

Arguments

Smooth AR coefficients and white noise variance — pw_smooth

<dl>

<dt>spatial</dt>
<dd>See <code>fit_bayesglm</code> internal code.</dd>


<dt>AR</dt>
<dd>A Vxp matrix of estimated AR coefficients, where V is the number of vertices and p is the AR model order</dd>


<dt>var</dt>
<dd>A vector length V containing the white noise variance estimates from the AR model</dd>


<dt>FWHM</dt>
<dd>FWHM parameter for smoothing. Remember that
\(\sigma = \frac{FWHM}{2*sqrt(2*log(2)}\). Set to <code>0</code> or <code>NULL</code>
to not do any smoothing. Default: <code>5</code>.</dd>

</dl>

pw_smooth: Smooth AR coefficients and white noise variance

Description

Usage

Value

Arguments