A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI-Reference-Cited by-同舟云学术

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A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

Published:2018-08-15 Issue:524 Volume:113 Page:1395-1410
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Yu Cheng-Han¹^ORCID,Prado Raquel¹,Ombao Hernando²,Rowe Daniel³

Affiliation:

1. Department of Applied Mathematics & Statistics, University of California at Santa Cruz, Santa Cruz, CA

2. Statistics Program, King Abdullah University of Science and Technology (KAUST), Saudi Arabia

3. Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, WI

Funder

Division of Mathematical Sciences

Division of Social and Economic Sciences

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2018.1476244

Reference52 articles.

1. Ricean over Gaussian modelling in magnitude fMRI analysis-added complexity with negligible practical benefits

2. Adrian, D. W., Maitra, R., and Rowe, D. B. (2017), “Complex-Valued Time-Series Modeling for Improved Accuracy of Activation Detection in fMRI Studies,”Annals of Applied Statistics, Accepted.

3. Time course EPI of human brain function during task activation

4. Optimal predictive model selection

5. Improved detectability in low signal-to-noise ratio magnetic resonance images by means of a phase-corrected real reconstruction

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2. A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data;Magnetic Resonance Imaging;2024-06

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4. Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data;The Neuroradiology Journal;2022-09-05

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