rm_nuisIC

Subtract estimated nuisance ICs from data matrix. If the number of
nuisance ICs is not provided, will estimate using PESEL (the nuisance ICs
are a rotation of the nuisance PCs).

internal

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

rm_nuisIC function

<dl><dt>BOLD</dt>
<dd>the row-centered \(V\) by \(T\) data</dd>
<dt>DR, prior_mean</dt>
<dd>We need an initial estimate of the brain networks, so
that we can avoid removing them during removal of the estimated noise ICs.
Provide either <code>DR</code> if dual regression has already been calculated, or
<code>prior_mean</code> (in this context, equivalent to a template result) if it
hasn't. Exactly one must be provided.</dd>
<dt>Q2</dt>
<dd>The number of nuisance ICs. If <code>NULL</code> (default) will estimate
using PESEL.</dd>
<dt>Q2_max</dt>
<dd>If <code>Q2</code> is <code>NULL</code>, PESEL's estimate will be less than
or equal to <code>Q2_max</code>. If <code>Q2_max</code> is <code>NULL</code> (default), do not
limit PESEL's estimate.</dd>
<dt>checkRowCenter</dt>
<dd>Check row means, and raise an error if they are
nonzero? Default: <code>TRUE</code>.</dd>
<dt>verbose</dt>
<dd>If <code>TRUE</code>, display progress updates.</dd>
<dt>return_Q2</dt>
<dd>Return (estimated) <code>Q2</code> too? Default: <code>FALSE</code>.</dd></dl>

Arguments

Remove nuisance ICs from data — rm_nuisIC

<dl>

<dt>BOLD</dt>
<dd>the row-centered \(V\) by \(T\) data</dd>


<dt>DR, prior_mean</dt>
<dd>We need an initial estimate of the brain networks, so
that we can avoid removing them during removal of the estimated noise ICs.
Provide either <code>DR</code> if dual regression has already been calculated, or
<code>prior_mean</code> (in this context, equivalent to a template result) if it
hasn't. Exactly one must be provided.</dd>


<dt>Q2</dt>
<dd>The number of nuisance ICs. If <code>NULL</code> (default) will estimate
using PESEL.</dd>


<dt>Q2_max</dt>
<dd>If <code>Q2</code> is <code>NULL</code>, PESEL's estimate will be less than
or equal to <code>Q2_max</code>. If <code>Q2_max</code> is <code>NULL</code> (default), do not
limit PESEL's estimate.</dd>


<dt>checkRowCenter</dt>
<dd>Check row means, and raise an error if they are
nonzero? Default: <code>TRUE</code>.</dd>


<dt>verbose</dt>
<dd>If <code>TRUE</code>, display progress updates.</dd>


<dt>return_Q2</dt>
<dd>Return (estimated) <code>Q2</code> too? Default: <code>FALSE</code>.</dd>

</dl>

rm_nuisIC: Remove nuisance ICs from data

Description

Usage

Value

Arguments