varTol_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

varTol_Param function

<dl><dt>varTol</dt>
<dd>Tolerance for variance of each data location. For each scan,
locations which do not meet this threshold are masked out of the analysis.
Default: <code>1e-6</code>. Variance is calculated on the original data, before
any normalization. Set to <code>0</code> to avoid removing locations due to
low variance.</dd></dl>

Arguments

varTol — varTol_Param

<dl>

<dt>varTol</dt>
<dd>Tolerance for variance of each data location. For each scan,
locations which do not meet this threshold are masked out of the analysis.
Default: <code>1e-6</code>. Variance is calculated on the original data, before
any normalization. Set to <code>0</code> to avoid removing locations due to
low variance.</dd>

</dl>

varTol

varTol_Param: varTol

Description

Arguments