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  "Title": "Estimate Brain Networks and Connectivity with Population-Derived\nPriors",
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  "Description": "Implements Bayesian brain mapping with population-derived\npriors, including the original model described in Mejia et al.\n(2020) <doi:10.1080/01621459.2019.1679638>, the model with\nspatial priors described in Mejia et al. (2022)\n<doi:10.1080/10618600.2022.2104289>, and the model with\npopulation-derived priors on functional connectivity described\nin Mejia et al. (2025) <doi:10.1093/biostatistics/kxaf022>.\nPopulation-derived priors are based on templates representing\nestablished brain network maps, for example derived from\nindependent component analysis (ICA), parcellations, or other\nmethods.  Model estimation is based on expectation-maximization\nor variational Bayes algorithms. Includes direct support for\n'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.",
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