TR_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

TR_param function

<dl><dt>TR</dt>
<dd>The temporal resolution of the data, i.e. the time between volumes,
in seconds. <code>TR</code> is required for detrending with <code>hpf</code>.</dd></dl>

Arguments

TR — TR_param

<dl>

<dt>TR</dt>
<dd>The temporal resolution of the data, i.e. the time between volumes,
in seconds. <code>TR</code> is required for detrending with <code>hpf</code>.</dd>

</dl>

TR_param: TR

Description

Arguments