plot.prior.gifti

Implements Bayesian brain mapping models, including the prior
ICA (independent components analysis) model proposed in Mejia et al. (2020)
<doi:10.1080/01621459.2019.1679638> and the spatial prior ICA model
proposed in proposed in Mejia et al. (2022)
<doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level
brain as deviations from known population-level networks, which are
estimated using standard ICA algorithms. Both models employ an
expectation-maximization algorithm for estimation of the latent brain
networks and unknown model parameters. 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

Daniel Spencer

Mary Beth Nebel

plot.prior.gifti function

<dl><dt>x</dt>
<dd>The prior from <code>estimate_prior.gifti</code></dd>
<dt>stat</dt>
<dd><code>"mean"</code>, <code>"sd"</code>, or <code>"both"</code> (default). By
default the square root of the variance prior is shown; another option is
<code>stat="var"</code> to instead display the variance prior directly.</dd>
<dt>var_method</dt>
<dd><code>"non-negative"</code> (default) or <code>"unbiased"</code></dd>
<dt>...</dt>
<dd>Additional arguments to <code>view_xifti</code></dd></dl>

Arguments

Plot prior — plot.prior.gifti

<dl>

<dt>x</dt>
<dd>The prior from <code>estimate_prior.gifti</code></dd>


<dt>stat</dt>
<dd><code>"mean"</code>, <code>"sd"</code>, or <code>"both"</code> (default). By
default the square root of the variance prior is shown; another option is
<code>stat="var"</code> to instead display the variance prior directly.</dd>


<dt>var_method</dt>
<dd><code>"non-negative"</code> (default) or <code>"unbiased"</code></dd>


<dt>...</dt>
<dd>Additional arguments to <code>view_xifti</code></dd>

</dl>

plot.prior.gifti: Plot prior

Description

Usage

Value

Arguments