Blind source separation (BSS) and independent component analysis (ICA) are generally
based on a wide class of unsupervised learning algorithms and they found potential …

Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI)
signal, complicating attempts to infer those changes that are truly related to brain activation …

We present an integrated approach to probabilistic independent component analysis (ICA)
for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian …

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-
based, linear representations of non-negative data. Although it has successfully been …

Recent advances in fluorescence imaging permit studies of Ca 2+ dynamics in large
numbers of cells, in anesthetized and awake behaving animals. However, unlike for …

Resting-state functional magnetic resonance imaging has become a powerful tool for the
study of functional networks in the brain. Even “at rest,” the brain's different functional …

Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to
motion and physiological sources, negatively impact the functional signal-to-noise ratio in …

The study of brain function is one of the most fascinating pursuits of m-ern science.
Functional neuroimaging is an important component of much of the current research in …

This short survey the reviews recent literature on brain connectivity studies. It encompasses
all forms of static and dynamic connectivity whether anatomical, functional, or effective. The …

Independent component analysis (ICA) is a statistical method used to discover hidden
factors (sources or features) from a set of measurements or observed data such that the …