scrub_Param_BayesGLM

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

scrub_Param_BayesGLM function

<dl><dt>scrub</dt>
<dd>(Optional) A \(T \times N_{scrub}\) matrix of spike regressors
(one 1 value at the timepoint to scrub, and 0 for all other values), or a
logical vector indicating the timepoints to scrub (<code>TRUE</code> to scrub, and
<code>FALSE</code> to keep). For multi-session data, a session-length list of
such matrices or logical vectors.
The spike regressors will be included in the nuisance
regression, and afterwards the timepoints indicated in <code>scrub</code> will be
removed from the BOLD data and design matrix.</dd></dl>

Arguments

scrub — scrub_Param_BayesGLM

<dl>

<dt>scrub</dt>
<dd>(Optional) A \(T \times N_{scrub}\) matrix of spike regressors
(one 1 value at the timepoint to scrub, and 0 for all other values), or a
logical vector indicating the timepoints to scrub (<code>TRUE</code> to scrub, and
<code>FALSE</code> to keep). For multi-session data, a session-length list of
such matrices or logical vectors.
The spike regressors will be included in the nuisance
regression, and afterwards the timepoints indicated in <code>scrub</code> will be
removed from the BOLD data and design matrix.</dd>

</dl>

scrub

scrub_Param_BayesGLM: scrub

Description

Arguments