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
.
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")
devtools::install_github("mandymejia/templateICAr")
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, and an
INLA-PARDISO license is highly recommended. INLA is NOT required for
running standard template ICA. 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()
.
To obtain an INLA-PARDISO license, run inla.pardiso()
in R after
running library(INLA)
. 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.