Standard neuroimaging data analysis based on traditional principles of experimental
design, modelling, and statistical inference is increasingly complemented by novel analysis …

Here we address an important issue that has been embedded within the neuroimaging
community for a long time: the absence of effect estimates in results reporting in the …

The “ten ironic rules for statistical reviewers” presented by Friston (2012) prompted a rebuttal
by Lindquist et al.(2013), which was followed by a rejoinder by Friston (2013). A key issue …

Predictive models ground many state-of-the-art developments in statistical brain image
analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach …

Multivariate linear methods are an important tool for the analysis of neuroimaging data.
However results obtained from multivariate methods are not as easy to interpret as results …

Over the past decade, multivariate “decoding analyses” have become a popular alternative
to traditional mass-univariate analyses in neuroimaging research. However, a fundamental …

Multivariate decoding methods were developed originally as tools to enable accurate
predictions in real-world applications. The realization that these methods can also be …

The demand for reproducibility has reached fever pitch in scientific research (Baker, 2015),
in particular in the field of psychology where many classic studies of human behaviour have …

In the past years, mass univariate statistical analyses of neuroimaging data have been
complemented by the use of multivariate pattern analyses, especially based on machine …

We introduce a data-analysis framework and performance metrics for evaluating and
optimizing the interaction between activation tasks, experimental designs, and the …