templateICAr-package: templateICAr: Estimate Brain Networks and Connectivity with...

In templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2019.1679638")} and the spatial template ICA model proposed in proposed in Mejia et al. (2022) \Sexpr[results=rd]{tools:::Rd_expr_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 [e-mail]

Authors:

• Damon Pham [e-mail] (ORCID)

Other contributors:

• Daniel Spencer [e-mail] (ORCID) [contributor]

• Mary Beth Nebel [e-mail] [contributor]

See Also

Useful links:

• https://github.com/mandymejia/templateICAr

• Report bugs at https://github.com/mandymejia/templateICAr/issues

templateICAr documentation built on Oct. 14, 2024, 5:08 p.m.