Bayesian estimation in random effects meta-analysis using a non-informative prior.

Pooling information from multiple, independent studies (meta-analysis) adds great value to medical research. Random effects models are widely used for this purpose. However, there are many different ways of estimating model parameters, and the choice of estimation procedure may be influential upon the conclusions of the meta-analysis. In this paper, we describe a recently proposed Bayesian estimation procedure and compare it with a profile likelihood method and with the DerSimonian-Laird and Mandel-Paule estimators including the Knapp-Hartung correction. The Bayesian procedure uses a non-informative prior for the overall mean and the between-study standard deviation that is determined by the Berger and Bernardo reference prior principle. The comparison of these procedures focuses on the frequentist properties of interval estimates for the overall mean. The results of our simulation study reveal that the Bayesian approach is a promising alternative producing more accurate interval estimates than those three conventional procedures for meta-analysis. The Bayesian procedure is also illustrated using three examples of meta-analysis involving real data. Copyright © 2016 John Wiley & Sons, Ltd.

Magnetoencephalography discriminates modality-specific infraslow signals less than 0.1 Hz.

DC-magnetoencephalography (DC-MEG) technique has been refined and allows to record cortical activity in the infraslow frequency range less than 0.1 Hz noninvasively. Important questions however, remained, especially, how specific these infraslow activations can be recorded and whether different activations, for example, motor versus acoustic, can be separated. To clarify these questions, in the present DC-MEG study, cortical infraslow activity was investigated intraindividually in response to different activation modalities, that is, motor versus acoustic: in 13 individuals, 30-s periods of finger movement or listening to concert music, were interleaved for 60 min. DC-MEG was capable to resolve intermodal differences concerning the relative amplitudes, field patterns, and source localizations. These results clarify that DC-MEG allows to identify and to discriminate modality-specific infraslow cortical neuronal signals.