The study of functional networks of the human brain has been of significant interest in
cognitive neuroscience for over two decades, albeit they are typically extracted at a single …

Human brain functional system has been viewed as a complex network. To accurately
characterize this brain network, it is important to estimate the functional connectivity between …

Objective: Sparse representations have been utilized to identify functional connectivity (FC)
of networks, while ICA employs the assumption of independence among the network …

Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and
reveal insights into interactions amongst different brain regions. However, most connectivity …

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI)
time series, especially during resting state periods, provides a powerful tool to assess …

Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn
increasing attention of neuroscientists and computer scientists, since it opens a new window …

To spatially cluster resting state‐functional magnetic resonance imaging (rs‐fMRI) data into
potential networks, there are only a few general approaches that determine the number of …

A fMRI connectivity analysis approach combining principal component analysis (PCA) and
regression analysis is proposed to detect functional connectivity between the brain regions …

Delineation of large-scale functional networks (FNs) from resting state functional MRI data
has become a standard tool to explore the functional brain organization in neuroscience …

Functional connectivity using resting-state fMRI has emerged as an important research tool
for understanding normal brain function as well as changes occurring during brain …