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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.
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Package details |
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| Author | Amanda Mejia [aut, cre], Damon Pham [aut] (<https://orcid.org/0000-0001-7563-4727>), Daniel Spencer [ctb] (<https://orcid.org/0000-0002-9705-3605>), Mary Beth Nebel [ctb] |
| Maintainer | Amanda Mejia <mandy.mejia@gmail.com> |
| License | GPL-3 |
| Version | 0.6.2 |
| URL | https://github.com/mandymejia/templateICAr |
| Package repository | View on CRAN |
| Installation |
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