Package: templateICAr 0.11.3

templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

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 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. Note, this package has been deprecated and superseded by 'BayesBrainMap', which includes model improvements and new names for the core functions.

Authors:Amanda Mejia [aut, cre], Damon Pham [aut], Daniel Spencer [ctb], Mary Beth Nebel [ctb]

templateICAr_0.11.3.tar.gz
templateICAr_0.11.3.zip(r-4.7-any)templateICAr_0.11.3.zip(r-4.6-any)templateICAr_0.11.3.zip(r-4.5-any)
templateICAr_0.11.3.tgz(r-4.6-any)templateICAr_0.11.3.tgz(r-4.5-any)
templateICAr_0.11.3.tar.gz(r-4.7-any)templateICAr_0.11.3.tar.gz(r-4.6-any)
templateICAr_0.11.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
templateICAr/json (API)

# Install 'templateICAr' in R:
install.packages('templateICAr', repos = c('https://mandymejia.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mandymejia/templateicar/issues

On CRAN:

Conda:

4.83 score 11 stars 31 scripts 512 downloads 17 exports 18 dependencies

Last updated from:455875c852. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK185
source / vignettesOK283
linux-releaseOK216
macos-releaseOK142
macos-oldrelOK136
windows-develOK134
windows-releaseOK121
windows-oldrelOK140
wasm-releaseOK144

Exports:activationsdim_reducedual_regdual_reg_parcestimate_templateestimate_template_FCestimate_template_from_DRexport_templategetInvCovARgroupICA.ciftimake_meshmake_mesh_2Dnorm_BOLDorthonormresample_templatesqrt_XtXtemplateICA

Dependencies:abindclasscodetoolsDEoptimRe1071fMRIscrubfMRItoolsforeachicaiteratorslatticeMASSMatrixmatrixStatspeselproxyrobustbaseSQUAREM

Readme and manuals

Help Manual

Help pageTopics
Activations of (spatial) template ICAactivations
Bdiag m2bdiag_m2
Cholesky-based FC samplingChol_samp_fun
PCA-based Dimension Reduction and Prewhiteningdim_reduce
Dual Regressiondual_reg
Multiple regression for parcel datadual_reg_parc
EM Algorithms for Template ICA ModelsEM_templateICA EM_templateICA.independent EM_templateICA.spatial
Universally estimate IW dof parameter nu based on method of moments, so that no empirical variance is under-estimatedestimate_nu
Estimate IW dof parameter nu based on method of momentsestimate_nu_matrix
Estimate templateestimate_template estimate_template.cifti estimate_template.gifti estimate_template.nifti
Estimate FC templateestimate_template_FC
Estimate template from DRestimate_template_from_DR
Estimation of effective sample sizeestimate.ESS
Export templateexport_template
Compute inverse covariance matrix for AR process (up to a constant scaling factor)getInvCovAR
Perform group ICA based on CIFTI datagroupICA.cifti
Compute theoretical Inverse-Wishart variance of covariance matrix elementsIW_var
Compute theoretical Inverse-Wishart variance of correlation matrix elementsIW_var_cor
Compute likelihood in SPDE model for ESS estimationlik
Make INLA mesh from '"surf"' objectmake_mesh
Make 2D INLA meshmake_mesh_2D
Normalize BOLD datanorm_BOLD
Orthonormalizes a square, invertible matrixorthonorm
Plot templateplot.template.cifti
Plot templateplot.template.gifti
Plot templateplot.template.matrix
Plot templateplot.template.nifti
Plot activationsplot.tICA_act.cifti
Plot templateplot.tICA.cifti
Plot templateplot.tICA.matrix
Plot templateplot.tICA.nifti
Estimate residual autocorrelation for prewhiteningpw_estimate
Resample CIFTI templateresample_template
Compute matrix square root of X'Xsqrt_XtX
Summarize a '"template.cifti"' objectprint.summary.template.cifti print.template.cifti summary.template.cifti
Summarize a '"template.gifti"' objectprint.summary.template.gifti print.template.gifti summary.template.gifti
Summarize a '"template.matrix"' objectprint.summary.template.matrix print.template.matrix summary.template.matrix
Summarize a '"template.nifti"' objectprint.summary.template.nifti print.template.nifti summary.template.nifti
Summarize a '"tICA_act.cifti"' objectprint.summary.tICA_act.cifti print.tICA_act.cifti summary.tICA_act.cifti
Summarize a '"tICA_act.matrix"' objectprint.summary.tICA_act.matrix print.tICA_act.matrix summary.tICA_act.matrix
Summarize a '"tICA_act.nifti"' objectprint.summary.tICA_act.nifti print.tICA_act.nifti summary.tICA_act.nifti
Summarize a '"tICA.cifti"' objectprint.summary.tICA.cifti print.tICA.cifti summary.tICA.cifti
Summarize a '"tICA.matrix"' objectprint.summary.tICA.matrix print.tICA.matrix summary.tICA.matrix
Summarize a '"tICA.nifti"' objectprint.summary.tICA.nifti print.tICA.nifti summary.tICA.nifti
Template ICAtemplateICA
Parameter Estimates in EM Algorithm for Template ICA ModelUpdateTheta_templateICA UpdateTheta_templateICA.independent UpdateTheta_templateICA.spatial
Transform upper-triangular elements to matrix formUT2mat
Compute the error between empirical and theoretical variance of covariance matrix elementsvar_sq_err
Compute the overall error between empirical and theoretical variance of CORRELATION matrix elementsvar_sq_err_constrained