Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

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

Representational similarity analysis of activation patterns has become an increasingly
important tool for studying brain representations. The dissimilarity between two patterns is …

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet
accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce …

A popular method for investigating whether stimulus information is present in fMRI response
patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate …

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI)
data are very successful at detecting brain regions that are activated as a whole during …

The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines
neural activity across large sets of small regions, exhaustively covering the whole brain. This …

Functional magnetic resonance imaging (fMRI) exploits blood‐oxygen‐level‐dependent
(BOLD) contrasts to map neural activity associated with a variety of brain functions including …

Classification of brain images obtained through functional magnetic resonance imaging
(fMRI) poses a serious challenge to pattern recognition and machine learning due to the …

Bayesian networks are a type of probabilistic graphical models lie at the intersection
between statistics and machine learning. They have been shown to be powerful tools to …