Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships
between measures of brain activity and of behavior or experimental design. In …

The goal of pattern-based classification of functional neuroimaging data is to link individual
brain activation patterns to the experimental conditions experienced during the scans. These …

We present an approach to efficiently detect the 2D pose of multiple people in an image. The
approach uses a nonparametric representation, which we refer to as Part Affinity Fields …

Recently, functional network connectivity (FNC, defined as the temporal correlation among
spatially distant brain networks) has been used to examine the functional organization of …

We demonstrate a hybrid machine learning method to classify schizophrenia patients and
healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide …

Neuroimaging-based diagnostics could potentially assist clinicians to make more accurate
diagnoses resulting in faster, more effective treatment. We participated in the 2011 ADHD …

Neuroimaging data often take the form of high-dimensional arrays, also known as tensors.
Addressing scientific questions arising from such data demands new regression models that …

STATIS is an extension of principal component analysis (PCA) tailored to handle multiple
data tables that measure sets of variables collected on the same observations, or …

In this study, a resting-state fMRI based classifier, for the first time, was proposed and
applied to discriminate children with attention-deficit/hyperactivity disorder (ADHD) from …

Major theories for explaining the organization of semantic memory in the human brain are
premised on the often-observed dichotomous dissociation between living and nonliving …