estimate_prior_FC_IW

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

estimate_prior_FC_IW function

<dl><dt>FC_mean_emp</dt>
<dd>Empirical FC mean estimate</dd>
<dt>FC_var_emp</dt>
<dd>Empirical FC variance estimate</dd>
<dt>nu_adjust</dt>
<dd>Factor by which to adjust estimate of nu. Values &lt; 1 will
inflate the prior variance to avoid an over-informative prior on FC.</dd></dl>

Arguments

Estimate IW FC prior — estimate_prior_FC_IW

<dl>

<dt>FC_mean_emp</dt>
<dd>Empirical FC mean estimate</dd>


<dt>FC_var_emp</dt>
<dd>Empirical FC variance estimate</dd>


<dt>nu_adjust</dt>
<dd>Factor by which to adjust estimate of nu. Values &lt; 1 will
inflate the prior variance to avoid an over-informative prior on FC.</dd>

</dl>

estimate_prior_FC_IW: Estimate IW FC prior

Description

Usage

Arguments