Abstract Analysis methods in cognitive neuroscience have not always matched the richness
of fMRI data. Early methods focused on estimating neural activity within individual voxels or …

Several research groups have shown how to map fMRI responses to the meanings of
presented stimuli. This paper presents new methods for doing so when only a natural …

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive
neuroscience researchers, large volumes of brain imaging data have been accumulated in …

Group studies involving large cohorts of subjects are important to draw general conclusions
about brain functional organization. However, the aggregation of data coming from multiple …

We explore transferring learning between fMRI datasets. A method is introduced to improve
prediction accuracy on a primary fMRI dataset by jointly learning a model using other …

Independent component analysis (ICA) is a blind source separation method for linear
disentanglement of independent latent sources from observed data. We investigate the …

Making sense of speech in a second language relies on multiple abilities. Differences in
brain activity related to proficiency in language tasks have often been attributed to …

Intelligent systems, whether biological or artificial, perceive unstructured information from the
world around them: deep neural networks designed for object recognition receive …

Multi-subject fMRI data analysis is an interesting and challenging problem in human brain
decoding studies. The inherent anatomical and functional variability across subjects make it …

One of the greatest challenges for the 21st century is understanding how the human brain
works. Although there are different levels of understanding of the human brain, a key step is …