Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its
predictive power. Such evaluation is achieved via cross-validation, a method also used to …

Human fMRI signals exhibit a spatial patterning that contains detailed information about a
person's mental states. Using classifiers it is possible to access this information and study …

Object recognition is a key function in both human and machine vision. While brain
decoding of seen and imagined objects has been achieved, the prediction is limited to …

A major challenge for systems neuroscience is to break the neural code. Computational
algorithms for encoding information into neural activity and extracting information from …

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying
neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and …

Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in
which real-time online fMRI signals are used to self-regulate brain function. Since its advent …

It is controversial whether the adult primate early visual cortex is sufficiently plastic to cause
visual perceptual learning (VPL). The controversy occurs partially because most VPL studies …

Abstract Machine learning is a powerful tool for creating computational models relating brain
function to behavior, and its use is becoming widespread in neuroscience. However, these …

There are growing numbers of studies using machine learning approaches to characterize
patterns of anatomical difference discernible from neuroimaging data. The high …

Perceptual experience consists of an enormous number of possible states. Previous fMRI
studies have predicted a perceptual state by classifying brain activity into prespecified …