Bayesian spatiotemporal modeling on complex‐valued fMRI signals via kernel convolutions-Reference-Cited by-同舟云学术

Bayesian spatiotemporal modeling on complex‐valued fMRI signals via kernel convolutions

Published:2022-03-09 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Yu Cheng‐Han¹^ORCID,Prado Raquel²,Ombao Hernando³,Rowe Daniel¹

Affiliation:

1. Department of Mathematical and Statistical Sciences Marquette University Milwaukee Wisconsin USA

2. Department of Statistics University of California Santa Cruz California USA

3. Statistics Program King Abdullah University of Science and Technology University Saudi Arabia

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13631

Reference42 articles.

1. Complex-valued time series modeling for improved activation detection in fMRI studies

2. Optimal predictive model selection

3. Bayesian Spatiotemporal Modeling Using Hierarchical Spatial Priors, with Applications to Functional Magnetic Resonance Imaging (with Discussion)

4. A Bayesian hierarchical framework for spatial modeling of fMRI data

5. Geostatistics without stationarity assumptions within geographical information systems;Brenning A.;Freiberg Online Geoscience,2001

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