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- W2980451348 abstract "Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure." @default.
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- W2980451348 date "2019-10-18" @default.
- W2980451348 modified "2023-09-27" @default.
- W2980451348 title "Anatomically informed Bayesian spatial priors for fMRI analysis" @default.
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