The brain is a complex, multiscale dynamical system composed of many interacting regions.
Knowledge of the spatiotemporal organization of these interactions is critical for establishing …

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been
widely used in both healthy subjects and patients with various neurologic, neurosurgical …

Abstract Machine learning techniques have gained prominence for the analysis of resting-
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …

Linking human behavior to resting-state brain function is a central question in systems
neuroscience. In particular, the functional timescales at which different types of behavioral …

Over the past two decades, resting-state functional connectivity (RSFC) methods have
provided new insights into the network organization of the human brain. Studies of brain …

Background The unparalleled performance of deep learning approaches in generic image
processing has motivated its extension to neuroimaging data. These approaches learn …

Given the dynamic nature of the brain, there has always been a motivation to move beyond
'static'functional connectivity, which characterizes functional interactions over an extended …

In the last few decades, brain functional connectivity (FC) has been extensively assessed
using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to …

Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging
modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects …