plot.tICA.nifti

Implements the template ICA (independent components analysis) model
proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the
spatial template 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

templateICAr

Estimate Brain Networks and Connectivity with ICA and Empirical
Priors

Damon Pham

Daniel Spencer

Mary Beth Nebel

plot.tICA.nifti function

<dl><dt>x</dt>
<dd>The result of <code>templateICA</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 template — plot.tICA.nifti

<dl>

<dt>x</dt>
<dd>The result of <code>templateICA</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.tICA.nifti: Plot template

Description

Usage

Value

Arguments