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.
estimate_template
. Can export the
results with export_template
.templateICA
.activations
.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")
You can install the development version of templateICAr
from Github with:
# install.packages("devtools")
::install_github("mandymejia/templateICAr") devtools
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.