Resting-state functional connectivity (RSFC) can be used for mapping large-scale human
brain networks during rest. There is considerable interest in distinguishing the individual …

Resting-state fMRI has become a powerful tool for studying network mechanisms of normal
brain functioning and its impairments by neurological and psychiatric disorders. Analytically …

Functional connectivity (FC) studies have predominantly focused on resting state, where
ongoing dynamics are thought to reflect the brain's intrinsic network architecture, which is …

Recent studies have suggested that resting-state brain functional connectivity (RSFC) has
the potential to discriminate among individuals in a population. These studies mostly utilized …

There is significant interest in using resting-state functional connectivity (RSFC) to predict
human behavior. Good behavioral prediction should in theory require RSFC to be …

Global signal regression (GSR) is one of the most debated preprocessing strategies for
resting-state functional MRI. GSR effectively removes global artifacts driven by motion and …

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest
is spatially organized into resting-state networks (RSNs). RSNs appear as groups of …

Dynamic functional connectivity (DFC) aims to maximize resolvable information from
functional brain scans by considering temporal changes in network structure. Recent work …

There is increasing use of functional imaging data to understand the macro-connectome of
the human brain. Of particular interest is the structure and function of intrinsic networks …

Background The analysis of brain imaging data often requires simplifying assumptions
because exhaustive analyses are computationally intractable. Standard univariate and …