templateICAr

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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.

  1. Template estimation: estimate_template. Can export the results with export_template.
  2. Template ICA model estimation (single-subject): templateICA.
  3. 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.