Chol_samp_fun

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

Chol_samp_fun function

<dl><dt>Chol_vals</dt>
<dd>Matrix of Cholesky factorizations (upper triangular values) for all prior sessions (nN*nM x nChol)</dd>
<dt>p</dt>
<dd>Pivot/reordering applied to FC matrices prior to Cholesky factorization</dd>
<dt>M</dt>
<dd>Number of samples to draw</dd>
<dt>chol_diag</dt>
<dd>Indices of diagonal upper triangular elements</dd>
<dt>chol_offdiag</dt>
<dd>Indices of off-diagonal upper triangular elements</dd>
<dt>Chol_mat_blank</dt>
<dd>A nLxnL matrix indexing the upper triangular elements</dd></dl>

Arguments

Cholesky-based FC sampling — Chol_samp_fun

<dl>

<dt>Chol_vals</dt>
<dd>Matrix of Cholesky factorizations (upper triangular values) for all prior sessions (nN*nM x nChol)</dd>


<dt>p</dt>
<dd>Pivot/reordering applied to FC matrices prior to Cholesky factorization</dd>


<dt>M</dt>
<dd>Number of samples to draw</dd>


<dt>chol_diag</dt>
<dd>Indices of diagonal upper triangular elements</dd>


<dt>chol_offdiag</dt>
<dd>Indices of off-diagonal upper triangular elements</dd>


<dt>Chol_mat_blank</dt>
<dd>A nLxnL matrix indexing the upper triangular elements</dd>

</dl>

Chol_samp_fun: Cholesky-based FC sampling

Description

Usage

Arguments