templateICAr

CRANtemplateICArMaRDI QIDQ79318

Estimate Brain Networks and Connectivity with ICA and Empirical Priors

Damon Đ Phạm, Amanda Mejia

Last update: 17 January 2024

Copyright license: GNU General Public License, version 3.0

Software version identifier: 0.6.2, 0.3.1, 0.6.4

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.

Cites work

• Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors
• Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference

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