templateICAr-package {templateICAr} | R Documentation |
templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors
Description
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.
Author(s)
Maintainer: Amanda Mejia mandy.mejia@gmail.com
Authors:
Damon Pham damondpham@gmail.com (ORCID)
Other contributors:
Daniel Spencer danieladamspencer@gmail.com (ORCID) [contributor]
Mary Beth Nebel Nebel@kennedykrieger.org [contributor]
See Also
Useful links:
Report bugs at https://github.com/mandymejia/templateICAr/issues