GSR_Param

internal

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

GSR_Param function

<dl><dt>GSR</dt>
<dd>Center BOLD across columns (each image)? This
is equivalent to performing global signal regression. Default:
<code>FALSE</code>.</dd></dl>

Arguments

GSR — GSR_Param

<dl>

<dt>GSR</dt>
<dd>Center BOLD across columns (each image)? This
is equivalent to performing global signal regression. Default:
<code>FALSE</code>.</dd>

</dl>

GSR_Param: GSR

Description

Arguments