mandymejia/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.

Getting started

Package details

MaintainerAmanda Mejia <mandy.mejia@gmail.com>
LicenseGPL-3
Version0.10.0
URL https://github.com/mandymejia/templateICAr
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("mandymejia/templateICAr")
mandymejia/templateICAr documentation built on June 2, 2025, 7:11 a.m.