… Priors advocated for volumetric fMRI are typically designed … multi-subject spatial Bayesian
modeling approach for fMRI … have very similar performance to a random effects model and, as …

… that these principles are not granular enough, so they are revisited in our discussion (8.11). …
of π0 it seems that they assume a within-variable mixture random effect, and not a between-…

… method to estimate such population prevalence that offers … (eg a General Linear Model of
fMRI data), a cross-validated out-of-… variance obtained from hierarchical mixed effects models. If …

… complemented by the corresponding effect-size estimates [2]. … of prevalence [3,4] beyond
the fixed and mixed (random) … than those assumed in classic random effect approaches, eg …

… , image-based statistical estimates from previously assumed … than those assumed in classic
random effect approaches, eg … prefer to work in terms of prevalence or accuracies, thus the …

… , image-based statistical estimates from previously assumed … of prevalence [3, 4] beyond the
fixed and mixed (random) … assumed in classic random effect approaches eg homogeneity in …

… More broadly, we advocate for approaches that are more … to “random effects” in conventional
linear mixed-effects models. … inherent to the approach, their prevalence makes it difficult to …

… GLM, including random effect models, and the ML framework in the estimation of optimum …
between classical GLM and ML-based prevalence inferences can be obtained using a linear …

… Also, theoretical detail is provided, and the use of this method in an fMRI decoding study is …
of the estimation procedure and this study does not cover the validation of the estimation. But, …

… Theoretical details of the i-test are provided, its high statistical power is identified by
numerical calculation, and the application of this method in an fMRI decoding is demonstrated. …