  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
