Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …

Answer questions and earn CME/CNE Over the past few decades, a body of research has
emerged confirming what many adult patients with noncentral nervous system cancer have …

Statistical machine learning methods are increasingly used for neuroimaging data analysis.
Their main virtue is their ability to model high-dimensional datasets, eg, multivariate analysis …

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

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

We present a high-dimensional model of the representational space in human ventral
temporal (VT) cortex in which dimensions are response-tuning functions that are common …

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

Interpreting brain image experiments requires analysis of complex, multivariate data. In
recent years, one analysis approach that has grown in popularity is the use of machine …

In 2001, we published a paper on the representation of faces and objects in ventral temporal
cortex that introduced a new method for fMRI analysis, which subsequently came to be …

Evidence of category specificity from neuroimaging in the human visual system is generally
limited to a few relatively coarse categorical distinctions—eg, faces versus bodies, or …