The potential of normative modeling to make individualized predictions from neuroimaging
data has enabled inferences that go beyond the case-control approach. However, site …

Neuroimaging faces the daunting challenge of multiple testing–an instance of multiplicity–
that is associated with two other issues to some extent: low inference efficiency and poor …

Studying small effects or subtle neuroanatomical variation requires large-scale sample size
data. As a result, combining neuroimaging data from multiple datasets is necessary …

To acquire larger samples for answering complex questions in neuroscience, researchers
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …

In Friston et al.((2002) Neuroimage 16: 465–483) we introduced empirical Bayes as a
potentially useful way to estimate and make inferences about effects in hierarchical models …

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data,
drawing appropriate statistical inferences is difficult due to the daunting task of accounting …

Functional MRI provides a unique perspective of neuronal organization; however, these
data include many complex sources of spatiotemporal variability, which require spatial …

While aggregation of neuroimaging datasets from multiple sites and scanners can yield
increased statistical power, it also presents challenges due to systematic scanner effects …

Here we address the current issues of inefficiency and over-penalization in the massively
univariate approach followed by the correction for multiple testing, and propose a more …

In the 1970s a novel branch of statistics emerged focusing its effort on the selection of a
function for the pattern recognition problem that would fulfill a relationship between the …