Analyzing functional magnetic resonance imaging (fMRI) time courses with dynamic
approaches has generated a great deal of interest because of the additional temporal …

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely
used to examine the brain's functional activities and has been recently used to characterize …

Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations
in functional connectivity by using various approaches including sliding window correlation …

Functional connectivities constructed via resting state fMRI (R-fMRI) data have been widely
used to study the brain's functional activities and to characterize the brain's states. However …

The study of functional brain networks has grown rapidly over the past decade. While most
functional connectivity (FC) analyses estimate one static network structure for the entire …

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to
understand the macro-connectome of the human brain. However, these fluctuations are not …

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine
recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of …

Dynamic functional connectivity (DFC) analysis has been widely applied to functional
magnetic resonance imaging (fMRI) data to reveal the time-varying functional interactions …

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple
metastable states of the inter-regional functional connectivity. Accordingly, the demand for …

Model-based data analysis of whole-brain dynamics links the observed data to model
parameters in a network of neural masses. Recently, studies focused on the role of regional …