Multivariate pattern analysis (MVPA) or brain decoding methods have become standard
practice in analyzing fMRI data. Although decoding methods have been extensively applied …

Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …

Over the past decade fMRI researchers have developed increasingly sensitive techniques
for analyzing the information represented in BOLD activity. The most popular of these …

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 …

Abstract Machine learning is increasingly being applied to neuroimaging data. However,
most machine learning algorithms have not been designed to accommodate neuroimaging …

This paper presents a comprehensive and practical review of autism spectrum disorder
(ASD) classification using several traditional machine learning and deep learning methods …

Can we decipher speech content (“what” is being said) and speaker identity (“who” is saying
it) from observations of brain activity of a listener? Here, we combine functional magnetic …

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

To what extent does functional brain organization rely on sensory input? Here, we show that
for the penultimate visual-processing region, ventral-temporal cortex (VTC), visual …

Multivariate pattern analysis (MVPA) has recently received increasing attention in functional
neuroimaging due to its ability to decode mental states from fMRI signals. However …