make_mesh

Create INLA mesh and observation weight matrix based on a <code>"surf"</code> object

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

make_mesh function

<dl><dt>surf</dt>
<dd>Object of class <code>"surf"</code>. See
<code>make_surf</code> and <code>is.surf</code>.</dd>
<dt>inds_data</dt>
<dd>Subset of vertices to include in analysis, e.g. non-medial
wall locations.</dd>
<dt>inds_mesh</dt>
<dd>Subset of vertices to retain in mesh, e.g. non-medial wall
locations. Must be a superset of <code>inds_data</code>.</dd></dl>

Arguments

Make INLA mesh from "surf" object — make_mesh

<dl>

<dt>surf</dt>
<dd>Object of class <code>"surf"</code>. See
<code>make_surf</code> and <code>is.surf</code>.</dd>


<dt>inds_data</dt>
<dd>Subset of vertices to include in analysis, e.g. non-medial
wall locations.</dd>


<dt>inds_mesh</dt>
<dd>Subset of vertices to retain in mesh, e.g. non-medial wall
locations. Must be a superset of <code>inds_data</code>.</dd>

</dl>

make_mesh: Make INLA mesh from `"surf"` object

Description

Usage

Value

Arguments