nuisance_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

nuisance_Param_BayesGLM function

<dl><dt>nuisance</dt>
<dd>(Optional) A \(T \times N_{nuis}\) matrix of nuisance signals,
where \(T\) is the number of timepoints and \(N\) is the number of
nuisance signals, or a list of these for multi-session analysis. Nuisance
signals are regressed from the fMRI data and design matrix prior to GLM
computation. Nuisance signals can include motion regressors, HRF derivatives
not being modeled as tasks, and other sources of noise.
Detrending/high-pass filtering is accomplished by adding DCT bases to the
nuisance matrix; see the parameters <code>hpf</code> and <code>DCT</code>.
Do not add spike regressors for scrubbing to the <code>nuisance</code> matrix.
Rather, provide these in <code>scrub</code> so that their corresponding timepoints
are also removed from the BOLD data after nuisance regression.</dd></dl>

Arguments

nuisance — nuisance_Param_BayesGLM

<dl>

<dt>nuisance</dt>
<dd>(Optional) A \(T \times N_{nuis}\) matrix of nuisance signals,
where \(T\) is the number of timepoints and \(N\) is the number of
nuisance signals, or a list of these for multi-session analysis. Nuisance
signals are regressed from the fMRI data and design matrix prior to GLM
computation. Nuisance signals can include motion regressors, HRF derivatives
not being modeled as tasks, and other sources of noise.
Detrending/high-pass filtering is accomplished by adding DCT bases to the
nuisance matrix; see the parameters <code>hpf</code> and <code>DCT</code>.
Do not add spike regressors for scrubbing to the <code>nuisance</code> matrix.
Rather, provide these in <code>scrub</code> so that their corresponding timepoints
are also removed from the BOLD data after nuisance regression.</dd>

</dl>

nuisance

nuisance_Param_BayesGLM: nuisance

Description

Arguments