estimate_prior_from_DR

Estimate variance decomposition and priors from DR estimates.

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

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