templateICAr

Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) and the spatial template ICA model proposed in proposed in Mejia et al. (2022) . 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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