Collection
Special Issue: Challenges in Computational Neuroscience
This special issue is dedicated to the Challenges in Computational Neuroscience program. In this issue, we are very excited to include four interesting articles, which represent a wide range of topics within computational neuroscience. These papers address several key problems in fMRI, MRI, rest-fMRI, and data integration, and comprehensively cover the wide spectrum of statistical methods in neuroscience research.
Editors
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Hongtu Zhu
PhD, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Thomas P. Witelski
PhD, Duke University, USA
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Tim Johnson
PhD, University of Illinois, USA
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Martin Linquist
PhD, Johns Hopkins University, USA
Articles (9 in this collection)
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Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies
Authors (first, second and last of 4)
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An Efficient Nonparametric Estimate for Spatially Correlated Functional Data
Authors (first, second and last of 4)
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A Cautionary Note When a Dose-Ranging Study is Used for Proving the Concept
Authors (first, second and last of 4)
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Brain Connectivity-Informed Regularization Methods for Regression
Authors (first, second and last of 7)
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NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data
Authors (first, second and last of 4)
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A Spatio-Temporal Model for Longitudinal Image-on-Image Regression
Authors (first, second and last of 5)
