Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
In this work, we identify a problem with the process of volume-to-surface mapping of functional Magn...
Previously we showed that network-based modelling of brain connectivity interacts strongly with the ...
Brain connectivity is often considered in terms of the communication between functionally distinct b...
Brain network data—measuring structural interconnections among brain regions of interest—are increas...
Brain network data—measuring anatomical interconnections among a common set of brain regions—are inc...
Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dy...
International audienceAnalysis of interactions in the brain in terms of functional resting-state net...
<div><p>Functional brain networks emerge and dissipate over a primarily static anatomical foundation...
Graph-theoretical methods have rapidly become a standard tool in studies of the structure and functi...
Contains fulltext : 190146.pdf (publisher's version ) (Open Access
Brain regions are often topographically connected: nearby locations within one brain area connect wi...
Modern imaging methods allow a non-invasive assessment of both structural and functional brain conne...
International audienceThe application of Graph Theory to the brain connectivity patterns obtained fr...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
In this work, we identify a problem with the process of volume-to-surface mapping of functional Magn...
Previously we showed that network-based modelling of brain connectivity interacts strongly with the ...
Brain connectivity is often considered in terms of the communication between functionally distinct b...
Brain network data—measuring structural interconnections among brain regions of interest—are increas...
Brain network data—measuring anatomical interconnections among a common set of brain regions—are inc...
Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dy...
International audienceAnalysis of interactions in the brain in terms of functional resting-state net...
<div><p>Functional brain networks emerge and dissipate over a primarily static anatomical foundation...
Graph-theoretical methods have rapidly become a standard tool in studies of the structure and functi...
Contains fulltext : 190146.pdf (publisher's version ) (Open Access
Brain regions are often topographically connected: nearby locations within one brain area connect wi...
Modern imaging methods allow a non-invasive assessment of both structural and functional brain conne...
International audienceThe application of Graph Theory to the brain connectivity patterns obtained fr...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neu...
In this work, we identify a problem with the process of volume-to-surface mapping of functional Magn...