update_S

Update S for VB FC Bayesian brain mapping

internal

Implements Bayesian brain mapping with population-derived priors,
including the original model described in Mejia et al. (2020)
<doi:10.1080/01621459.2019.1679638>, the model with spatial priors described
in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model
with population-derived priors on functional connectivity described in Mejia
et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors
are based on templates representing established brain network maps, for
example derived from independent component analysis (ICA), parcellations,
or other methods.  Model estimation is based on expectation-maximization or
variational Bayes algorithms. 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

Nohelia Da Silva

update_S function

<dl><dt>mu_tau2, mu_A, E_AtA, </dt>
<dd>Most recent posterior estimates</dd>
<dt>D_inv, D_inv_S</dt>
<dd>Some pre-computed quantities.</dd>
<dt>BOLD</dt>
<dd>(\(T \times V\) matrix) preprocessed fMRI data.</dd>
<dt>nNets, nvox, ntime</dt>
<dd>Number of networks, number of data locations, and time series length</dd>
<dt>final</dt>
<dd>If TRUE, return cov_S. Default: <code>FALSE</code></dd></dl>

Arguments

Update S for VB FC Bayesian brain mapping — update_S

<dl>

<dt>mu_tau2, mu_A, E_AtA, </dt>
<dd>Most recent posterior estimates</dd>


<dt>D_inv, D_inv_S</dt>
<dd>Some pre-computed quantities.</dd>


<dt>BOLD</dt>
<dd>(\(T \times V\) matrix) preprocessed fMRI data.</dd>


<dt>nNets, nvox, ntime</dt>
<dd>Number of networks, number of data locations, and time series length</dd>


<dt>final</dt>
<dd>If TRUE, return cov_S. Default: <code>FALSE</code></dd>

</dl>

update_S: Update S for VB FC Bayesian brain mapping

Description

Usage

Value

Arguments