scale_Param

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

scale_Param function

<dl><dt>scale</dt>
<dd><code>"local"</code> (default), <code>"global"</code>, or <code>"none"</code>.
Local scaling will divide each data location's time series by its estimated
standard deviation. Global scaling will divide the entire data matrix by the
mean image standard deviation (<code>mean(sqrt(rowVars(BOLD)))</code>).</dd></dl>

Arguments

scale — scale_Param

<dl>

<dt>scale</dt>
<dd><code>"local"</code> (default), <code>"global"</code>, or <code>"none"</code>.
Local scaling will divide each data location's time series by its estimated
standard deviation. Global scaling will divide the entire data matrix by the
mean image standard deviation (<code>mean(sqrt(rowVars(BOLD)))</code>).</dd>

</dl>

scale

scale_Param: scale

Description

Arguments