Package: templateICAr 0.9.1
Amanda Mejia
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 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.
Authors:
templateICAr_0.9.1.tar.gz
templateICAr_0.9.1.tar.gz(r-4.5-noble)templateICAr_0.9.1.tar.gz(r-4.4-noble)
templateICAr_0.9.1.tgz(r-4.4-emscripten)templateICAr_0.9.1.tgz(r-4.3-emscripten)
templateICAr.pdf |templateICAr.html✨
templateICAr/json (API)
NEWS
| # Install 'templateICAr' in R: |
| install.packages('templateICAr', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/mandymejia/templateicar/issues0 issues
Last updated 4 months agofrom:663f14aa5b. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 25 2025 |
| R-4.5-linux | OK | Mar 25 2025 |
| R-4.4-linux | OK | Mar 25 2025 |
Exports:activationsdim_reduceestimate_templateestimate_template_FCestimate_template_from_DRexport_templategetInvCovARgroupICA.ciftimake_meshmake_mesh_2Dnorm_BOLDorthonormresample_templatesqrt_XtXtemplateICA
Dependencies:abindcellWiseclassclicodetoolscolorspaceDEoptimRe1071expmfansifarverfMRIscrubfMRItoolsforeachgamlssgamlss.datagamlss.distggplot2gluegridExtragtableicaisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnlmepcaPPpeselpillarpkgconfigplyrproxyR6RColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserrcovscalesshapeSQUAREMstringistringrsurvivalsvdtibbleutf8vctrsviridisLitewithr
Citation
To cite {templateICAr} in publications use:
Mejia AF, Nebel MB, Wang Y, Caffo BS, Guo Y (2020). “Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors.” Journal of the American Statistical Association, 115(531), 1151–1177. doi:10.1080/01621459.2019.1679638.
Mejia AF, Bolin D, Yue YR, Wang J, Caffo BS, Nebel MB (2022). “Template Independent Component Analysis with spatial priors for accurate subject-level brain network estimation and inference.” Journal of Computational and Graphical Statistics, 1–35. doi:10.1080/10618600.2022.2104289.
Corresponding BibTeX entries:
@Article{,
title = {{T}emplate {I}ndependent {C}omponent {A}nalysis: Targeted
and Reliable Estimation of Subject-level Brain Networks Using Big
Data Population Priors},
author = {Amanda F Mejia and Mary Beth Nebel and Yikai Wang and
Brian S Caffo and Ying Guo},
journal = {Journal of the American Statistical Association},
year = {2020},
volume = {115},
number = {531},
pages = {1151--1177},
publisher = {American Statistical Association},
doi = {10.1080/01621459.2019.1679638},
}
@Article{,
title = {{T}emplate {I}ndependent {C}omponent {A}nalysis with
spatial priors for accurate subject-level brain network
estimation and inference},
author = {Amanda F Mejia and David Bolin and Yu Ryan Yue and
Jiongran Wang and Brian S Caffo and Mary Beth Nebel},
journal = {Journal of Computational and Graphical Statistics},
year = {2022},
number = {just-accepted},
pages = {1--35},
publisher = {American Statistical Association},
doi = {10.1080/10618600.2022.2104289},
}
Readme and manuals
templateICAr
This package contains functions implementing the template ICA model proposed in Mejia et al. (2019) and the spatial template ICA model proposed in proposed in Mejia et al. (2020+). For both models, subject-level brain networks are estimated as deviations from known population-level networks, which can be estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters.
Template ICA consists of three steps. The main functions associated with each step are listed below.
- Template estimation:
estimate_template. Can export the results withexport_template. - Template ICA model estimation (single-subject):
templateICA. - Identification of areas of engagement in each IC (or deviation from
the template mean):
activations.
Citation
If you use templateICAr please cite the following papers:
| Name | APA Citation |
|---|---|
| Template ICA | Mejia, A. F., Nebel, M. B., Wang, Y., Caffo, B. S., & Guo, Y. (2020). Template Independent Component Analysis: targeted and reliable estimation of subject-level brain networks using big data population priors. Journal of the American Statistical Association, 115(531), 1151-1177. |
| Spatial Template ICA | Mejia, A. F., Bolin, D., Yue, Y. R., Wang, J., Caffo, B. S., & Nebel, M. B. (2022). Template Independent Component Analysis with spatial priors for accurate subject-level brain network estimation and inference. Journal of Computational and Graphical Statistics, (just-accepted), 1-35. |
You can also obtain citation information from within R like so:
citation("templateICAr")
Installation
You can install the development version of templateICAr from Github
with:
# install.packages("devtools")
devtools::install_github("mandymejia/templateICAr")
Important Notes on Dependencies:
To analyze or visualize CIFTI-format data, templateICAr depends on the
ciftiTools package, which requires an installation of Connectome
Workbench. It can be installed from the HCP
website.
For fitting the template ICA model with surface-based priors
(spatial_model=TRUE in templateICA()), INLA is required. Due to a
CRAN policy, INLA cannot be installed automatically. You can obtain it
by running
install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE).
Alternatively, dep=FALSE can be used along with manual installation of
dependencies as necessary to avoid installing all of the many INLA
dependencies, most of which are not actually required. Binaries for
alternative Linux builds can be added with the command
inla.binary.install(). Note that INLA is not required for standard
template ICA.
Depending on the analysis, PARDISO may reduce computation time. To
obtain a free academic license forINLA-PARDISO, run inla.pardiso() in
R after running library(INLA). Provide an academic email address. Once
you obtain a license, point to it using
INLA::inla.setOption(pardiso.license = "pardiso.lic") followed by
INLA::inla.pardiso.check() to ensure that PARDISO is successfully
installed and running.
