plot.bMap.nifti

Implements Bayesian brain mapping with population-derived priors,
including the original model described in Mejia et al. (2020)
<doi:10.1080/01621459.2019.1679638>, the model with spatial priors described
in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model
with population-derived priors on functional connectivity described in Mejia
et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors
are based on templates representing established brain network maps, for
example derived from independent component analysis (ICA), parcellations,
or other methods.  Model estimation is based on expectation-maximization or
variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI',
and 'NIFTI' neuroimaging file formats.

Amanda Mejia

BayesBrainMap

Estimate Brain Networks and Connectivity with Population-Derived
Priors

Damon Pham

Nohelia Da Silva

plot.bMap.nifti function

<dl><dt>x</dt>
<dd>The result of <code>fit_BBM</code> with NIFTI data</dd>
<dt>stat</dt>
<dd><code>"mean"</code> (default), <code>"se"</code></dd>
<dt>plane, n_slices, slices</dt>
<dd>Anatomical plane and which slice indices to show.
Default: 9 axial slices.</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Plot prior — plot.bMap.nifti

<dl>

<dt>x</dt>
<dd>The result of <code>fit_BBM</code> with NIFTI data</dd>


<dt>stat</dt>
<dd><code>"mean"</code> (default), <code>"se"</code></dd>


<dt>plane, n_slices, slices</dt>
<dd>Anatomical plane and which slice indices to show.
Default: 9 axial slices.</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

plot.bMap.nifti: Plot prior

Description

Usage

Value

Arguments