Calculate the HRF from a time vector and parameters. Optionally compute the
first or second derivative of the HRF instead.

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

Daniel Spencer

Damon Pham

David Bolin

Sarah Ryan

Yu (Ryan) Yue

HRF function

<dl><dt>t</dt>
<dd>time vector</dd>
<dt>deriv</dt>
<dd><code>0</code> (default) for the HRF, <code>1</code> for the first derivative
of the HRF, or <code>2</code> for the second derivative of the HRF.</dd>
<dt>a1</dt>
<dd>delay of response. Default: <code>6</code></dd>
<dt>b1</dt>
<dd>response dispersion. Default: <code>0.9</code></dd>
<dt>a2</dt>
<dd>delay of undershoot. Default: <code>12</code></dd>
<dt>b2</dt>
<dd>dispersion of undershoot. Default: <code>0.9</code></dd>
<dt>c</dt>
<dd>scale of undershoot. Default: <code>0.35</code></dd></dl>

Arguments

Canonical (double-gamma) HRF — HRF

<dl>

<dt>t</dt>
<dd>time vector</dd>


<dt>deriv</dt>
<dd><code>0</code> (default) for the HRF, <code>1</code> for the first derivative
of the HRF, or <code>2</code> for the second derivative of the HRF.</dd>


<dt>a1</dt>
<dd>delay of response. Default: <code>6</code></dd>


<dt>b1</dt>
<dd>response dispersion. Default: <code>0.9</code></dd>


<dt>a2</dt>
<dd>delay of undershoot. Default: <code>12</code></dd>


<dt>b2</dt>
<dd>dispersion of undershoot. Default: <code>0.9</code></dd>


<dt>c</dt>
<dd>scale of undershoot. Default: <code>0.35</code></dd>

</dl>

HRF: Canonical (double-gamma) HRF

Description

Usage

Value

Arguments

Examples