Spatial Sparsity
Cross-source consensus on Spatial Sparsity from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Comparisons
Highlighted claims
- Spatial dependence is modeled through local shrinkage scale parameters. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
- Neighboring voxels are encouraged to share activation patterns while sparse variable selection is preserved. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
- Voxel neighbors are defined by shared faces, giving each three-dimensional voxel at most six neighbors. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
- BNR activation maps showed stronger spatial coherence than ISC maps in the movie-viewing analysis. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
- The checkerboard study found BNR maps had somewhat stronger spatial coherence than GLM maps. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data