A new Bayesian model with sparse Gaussian processes and horseshoe priors estimates shared neural responses and voxel activations in fMRI data with better uncertainty quantification than intersubject correlation methods.
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Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
A new Bayesian model with sparse Gaussian processes and horseshoe priors estimates shared neural responses and voxel activations in fMRI data with better uncertainty quantification than intersubject correlation methods.