Special section on statistics in neuroscience. (English) Zbl 1236.62153

Kafadar, Karen

Ann. Appl. Stat. 5, No. 2B, 1127-1131 (2011).

Summary: This article provides a brief introduction to seven papers that are included in this special section on statistics in neuroscience:
(1) X. Shi, J.G. Ibrahim, J. Lieberman, M. Styner, Y. Li and H. Zhu: Two-state empirical likelihood for longitudinal neuroimaging data.
(2) V.Q. Vu, P. Ravikumar, T. Naselaris, K.N. Kay, J.L. Gallant and B. Yu: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models.
(3) S. Bhattacharya and R. Maitra: A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments.
(4) C.J. Long, P.L. Purdon, S. Temereanca, N.U. Desai, M.S. Hämäläinen and E.N. Brown: State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing.
(5) Y. Mishchenko, J.T. Vogelstein and L. Paninski: A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data.
(6) R.E. Kass, R.C. Kelly and W.-L. Loh: Assessment of synchrony in multiple neural spike trains using loglinear point process models.
(7) S. Olhede and B. Whitcher: Nonparametric tests of structure for high angular resolution diffusion imaging in Q-space.

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
92C20	Neural biology
92C55	Biomedical imaging and signal processing

Keywords:

functional magnetic resonance imaging; fMRI; brain imaging; exploratory analysis; nonparametric fitting; model selection; signal detection

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