estimate_prior_from_DR

Estimate variance decomposition and priors from DR estimates.

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_from_DR function

<dl><dt>DR</dt>
<dd>the test/retest dual regression estimates, as an array with
dimensions \(M \times N \times (L \times V)\), where \(M\) is the number
of visits (2), \(N\) is the number of subjects, \(L\) is the number of
brain networks, and \(V\) is the number of data locations.
(\(L\) and \(V\) are collapsed because they are treated equivalently
in the context of calculating the variance decomposition and priors).</dd>
<dt>LV</dt>
<dd>A length-two integer vector giving the dimensions \(L\) and
\(V\) to reshape the result. Default: <code>NULL</code> (do not reshape the
result).</dd></dl>

Arguments

Estimate prior from DR — estimate_prior_from_DR

<dl>

<dt>DR</dt>
<dd>the test/retest dual regression estimates, as an array with
dimensions \(M \times N \times (L \times V)\), where \(M\) is the number
of visits (2), \(N\) is the number of subjects, \(L\) is the number of
brain networks, and \(V\) is the number of data locations.
(\(L\) and \(V\) are collapsed because they are treated equivalently
in the context of calculating the variance decomposition and priors).</dd>


<dt>LV</dt>
<dd>A length-two integer vector giving the dimensions \(L\) and
\(V\) to reshape the result. Default: <code>NULL</code> (do not reshape the
result).</dd>

</dl>

estimate_prior_from_DR: Estimate prior from DR

Description

Usage

Value

Arguments