GLM_classical

Classical GLM for fit_bayesglm (internal function)

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

GLM_classical function

<dl><dt>BOLD</dt>
<dd>BOLD timeseries in vector form (TVx1), result of <code>sparse_and_PW</code></dd>
<dt>design</dt>
<dd>List of large sparse design matrices (each TVxV), one per regressor, result of <code>sparse_and_PW</code></dd>
<dt>nK2, nV_input, field_names, design_type</dt>
<dd>See <code>fit_bayesglm</code>.</dd>
<dt>valid_cols, nT</dt>
<dd>See <code>fit_bayesglm</code>.</dd>
<dt>do_pw</dt>
<dd>Has prewhitening been performed on the data and design?</dd>
<dt>compute_SE</dt>
<dd>Compute SE of model coefficients?</dd></dl>

Arguments

Classical GLM — GLM_classical

<dl>

<dt>BOLD</dt>
<dd>BOLD timeseries in vector form (TVx1), result of <code>sparse_and_PW</code></dd>


<dt>design</dt>
<dd>List of large sparse design matrices (each TVxV), one per regressor, result of <code>sparse_and_PW</code></dd>


<dt>nK2, nV_input, field_names, design_type</dt>
<dd>See <code>fit_bayesglm</code>.</dd>


<dt>valid_cols, nT</dt>
<dd>See <code>fit_bayesglm</code>.</dd>


<dt>do_pw</dt>
<dd>Has prewhitening been performed on the data and design?</dd>


<dt>compute_SE</dt>
<dd>Compute SE of model coefficients?</dd>

</dl>

GLM_classical: Classical GLM

Description

Usage

Value

Arguments