compute_R_inv

Compute SPDE matrices (F, G and GFinvG) and prior precision matrix for S

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

compute_R_inv function

<dl><dt>meshes</dt>
<dd><code>NULL</code> for spatial independence model, otherwise a list of
objects of class "BrainMap_mesh" containing the triangular mesh (see
<code>make_mesh</code>) for each brain structure.</dd>
<dt>kappa</dt>
<dd>Current estimates of SPDE parameter kappa for each latent field</dd>
<dt>C1</dt>
<dd>Constant, equal to \(1/(4*pi)\) for a 2-dimensional field with alpha=2</dd>
<dt>rm_extra</dt>
<dd>If <code>TRUE</code>. remove extra (non-data) vertices from the mesh for greater computational efficiency</dd></dl>

Arguments

Compute SPDE and prior precision matrices for S — compute_R_inv

<dl>

<dt>meshes</dt>
<dd><code>NULL</code> for spatial independence model, otherwise a list of
objects of class "BrainMap_mesh" containing the triangular mesh (see
<code>make_mesh</code>) for each brain structure.</dd>


<dt>kappa</dt>
<dd>Current estimates of SPDE parameter kappa for each latent field</dd>


<dt>C1</dt>
<dd>Constant, equal to \(1/(4*pi)\) for a 2-dimensional field with alpha=2</dd>


<dt>rm_extra</dt>
<dd>If <code>TRUE</code>. remove extra (non-data) vertices from the mesh for greater computational efficiency</dd>

</dl>

compute_R_inv: Compute SPDE and prior precision matrices for S

Description

Usage

Value

Arguments