TL;DR
This special section introduces seven papers that apply advanced statistical methods to various neuroscience data types, including neuroimaging, fMRI, MEG, calcium imaging, and diffusion imaging, highlighting recent methodological developments.
Contribution
The section showcases novel statistical approaches tailored for neuroscience data analysis, emphasizing nonparametric, Bayesian, and high-performance computing techniques.
Findings
Improved models for longitudinal neuroimaging data
Sparse nonparametric models for fMRI responses
Bayesian methods for neuronal connectivity inference
Abstract
This article provides a brief introduction to seven papers that are included in this special section on Statistics in Neuroscience: (1) Xiaoyan Shi, Joseph G. Ibrahim, Jeffrey Lieberman, Martin Styner, Yimei Li and Hongtu Zhu: Two-state empirical likelihood for longitudinal neuroimaging data (2) Vincent Q. Vu, Pradeep Ravikumar, Thomas Naselaris, Kendrick N. Kay, Jack L. Gallant and Bin Yu: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models (3) Sourabh Bhattacharya and Ranjan Maitra: A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments (4) Christopher J. Long, Patrick L. Purdon, Simona Temereanca, Neil U. Desai, Matti S. H\"{a}m\"{a}l\"{a}inen and Emery Neal Brown: State-space solutions to the dynamic magnetoencephalography inverse problem using high…
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