Functional network (FN) analyses play a pivotal role in uncovering insights into brain
function and understanding the pathophysiology of various brain disorders. This paper …

Progress in scientific disciplines is accompanied by standardization of terminology. Network
neuroscience, at the level of macroscale organization of the brain, is beginning to confront …

Progress in scientific disciplines is often accompanied by the standardization of terminology
and nomenclature. Network neuroscience, as applied at the level of macro-scale …

Independent component analysis (ICA) is a widely used blind source separation method for
signal pre-processing. The determination of the number of independent components (ICs) is …

Functional networks (FNs) hold significant promise in understanding brain function.
Independent component analysis (ICA) has been applied in estimating FNs from functional …

Resting-state functional magnetic resonance imaging is currently the mainstay of functional
neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka …

Brain functional networks and connectivity have played an important role in exploring brain
function for understanding the brain and disclosing the mechanisms of brain disorders …

Independent component analysis (ICA) is a blind source signal processing technique that
separates the source signal from the mixed signal. ICA makes great progress in researches …

Independent component analysis (ICA) has been widely applied to estimate brain functional
networks from functional magnetic resonance imaging (fMRI) data. ICA is a data-driven …

Connectivity within the human connectome occurs between multiple neuronal systems—at
small to very large spatial scales. Independent component analysis (ICA) is potentially a …