Special section on statistics in neuroscience

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 performance computing (5) Yuriy Mishchencko, Joshua T. Vogelstein and Liam Paninski: A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data (6) Robert E. Kass, Ryan C. Kelly and Wei-Liem Loh: Assessment of synchrony in multiple neural spike trains using loglinear point process models (7) Sofia Olhede and Brandon Whitcher: Nonparametric tests of structure for high angular resolution diffusion imaging in Q-space ; Comment: Published in at http://dx.doi.org/10.1214/11-AOAS485 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)