Several independent studies have demonstrated that small amounts of in-scanner motion
systematically bias estimates of resting-state functional connectivity. This confound is of …

Brain signals measured over a series of experiments have inherent variability because of
different physical and mental conditions among multiple subjects and sessions. Such …

Resting‐state functional connectivity (FC) is highly variable across the duration of a scan.
Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been …

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via
predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model …

Background Brain networks in fMRI are typically identified using spatial independent
component analysis (ICA), yet other mathematical constraints provide alternate biologically …

Recent studies of functional connectivity MR imaging have revealed that the default-mode
network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is …

Sequential dictionary learning algorithms have been successfully applied to functional
magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured …

A single-task functional magnetic resonance imaging (fMRI) experiment may only partially
highlight alterations to functional brain networks affected by a particular disorder …

Brain functional connectome analysis is commonly based on population‐wise inference.
However, in this way precious information provided at the individual subject level may be …

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 …