compute_mu_s

Compute posterior mean and precision matrix of s

internal

Implements the template ICA (independent components analysis) model
proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the
spatial template 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

templateICAr

Estimate Brain Networks and Connectivity with ICA and Empirical
Priors

Damon Pham

Daniel Spencer

Mary Beth Nebel

compute_mu_s function

<dl><dt>y_vec</dt>
<dd>Vectorized, dimension-reduced fMRI data, grouped by locations. A vector of length \(QV\).</dd>
<dt>D</dt>
<dd>Sparse diagonal matrix of template standard deviations</dd>
<dt>Dinv_s0</dt>
<dd>The inverse of D times s0_vec</dd>
<dt>R_inv</dt>
<dd>Estimate of inverse spatial correlation matrix (sparse)</dd>
<dt>theta</dt>
<dd>List of current parameter estimates</dd>
<dt>P</dt>
<dd>Permutation matrix for regrouping by locations (instead of by ICs.)</dd>
<dt>C_diag</dt>
<dd>Diagonals of residual covariance of the first level model. A vector of length \(Q\).</dd></dl>

Arguments

Compute posterior mean and precision of s — compute_mu_s

<dl>

<dt>y_vec</dt>
<dd>Vectorized, dimension-reduced fMRI data, grouped by locations. A vector of length \(QV\).</dd>


<dt>D</dt>
<dd>Sparse diagonal matrix of template standard deviations</dd>


<dt>Dinv_s0</dt>
<dd>The inverse of D times s0_vec</dd>


<dt>R_inv</dt>
<dd>Estimate of inverse spatial correlation matrix (sparse)</dd>


<dt>theta</dt>
<dd>List of current parameter estimates</dd>


<dt>P</dt>
<dd>Permutation matrix for regrouping by locations (instead of by ICs.)</dd>


<dt>C_diag</dt>
<dd>Diagonals of residual covariance of the first level model. A vector of length \(Q\).</dd>

</dl>

compute_mu_s: Compute posterior mean and precision of s

Description

Usage

Value

Arguments