Compute likelihood in SPDE model for ESS estimation

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

lik function

<dl><dt>theta</dt>
<dd>Value of hyperparameters</dd>
<dt>Y</dt>
<dd>Data vector</dd>
<dt>G</dt>
<dd>SPDE G matrix</dd>
<dt>C</dt>
<dd>SPDE C matrix</dd>
<dt>ind</dt>
<dd>Indices of data locations in the mesh</dd></dl>

Arguments

Compute likelihood in SPDE model for ESS estimation — lik

<dl>

<dt>theta</dt>
<dd>Value of hyperparameters</dd>


<dt>Y</dt>
<dd>Data vector</dd>


<dt>G</dt>
<dd>SPDE G matrix</dd>


<dt>C</dt>
<dd>SPDE C matrix</dd>


<dt>ind</dt>
<dd>Indices of data locations in the mesh</dd>

</dl>

lik: Compute likelihood in SPDE model for ESS estimation

Description

Usage

Value

Arguments