Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain
Functional magnetic resonance data acquired in a task-absent condition (“resting state”)
require new data analysis techniques that do not depend on an activation model. In this …
require new data analysis techniques that do not depend on an activation model. In this …
Fast eigenvector centrality mapping of voxel-wise connectivity in functional magnetic resonance imaging: implementation, validation, and interpretation
Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially
characterize connectivity in functional brain imaging by attributing network properties to …
characterize connectivity in functional brain imaging by attributing network properties to …
Assessing functional connectivity in the human brain by fMRI
Functional magnetic resonance imaging (fMRI) is widely used to detect and delineate
regions of the brain that change their level of activation in response to specific stimuli and …
regions of the brain that change their level of activation in response to specific stimuli and …
Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data
FT Sun, LM Miller, M D'esposito - Neuroimage, 2004 - Elsevier
Understanding functional connectivity within the brain is crucial to understanding neural
function; even the simplest cognitive operations are supported by highly distributed neural …
function; even the simplest cognitive operations are supported by highly distributed neural …
Network scaling effects in graph analytic studies of human resting-state FMRI data
Graph analysis has become an increasingly popular tool for characterizing topological
properties of brain connectivity networks. Within this approach, the brain is modeled as a …
properties of brain connectivity networks. Within this approach, the brain is modeled as a …
Groupwise whole-brain parcellation from resting-state fMRI data for network node identification
In this paper, we present a groupwise graph-theory-based parcellation approach to define
nodes for network analysis. The application of network-theory-based analysis to extend the …
nodes for network analysis. The application of network-theory-based analysis to extend the …
The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery
Recent years have witnessed a rapid growth of interest in moving functional magnetic
resonance imaging (fMRI) beyond simple scan-length averages and into approaches that …
resonance imaging (fMRI) beyond simple scan-length averages and into approaches that …
[HTML][HTML] Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
L Geerligs, RN Henson - NeuroImage, 2016 - Elsevier
Studies of brain-wide functional connectivity or structural covariance typically use measures
like the Pearson correlation coefficient, applied to data that have been averaged across …
like the Pearson correlation coefficient, applied to data that have been averaged across …
Effects of spatial smoothing on functional brain networks
Graph‐theoretical methods have rapidly become a standard tool in studies of the structure
and function of the human brain. Whereas the structural connectome can be fairly …
and function of the human brain. Whereas the structural connectome can be fairly …
Replicability of time-varying connectivity patterns in large resting state fMRI samples
The past few years have seen an emergence of approaches that leverage temporal changes
in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome …
in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome …