Elsevier

NeuroImage

Volume 180, Part B, 15 October 2018, Pages 335-336
NeuroImage

Editorial
Mapping and interpreting the dynamic connectivity of the brain

https://doi.org/10.1016/j.neuroimage.2018.07.018Get rights and content

Section snippets

Dynamic graphs

One of the challenges in dynamic analysis of neuroimaging data is how to handle the explosion of data that results. One promising approach is provided by graph theory, a framework in which the brain's functional connectivity is described in terms of nodes that map to discrete regions of the brain, which are connected by weighted edges that describe the strength of the connection. The graph can then be described by summary measures (e.g., modularity, efficiency), providing an economical

Biophysical models

The view of the brain as a dynamic network also lends itself to interrogation based on modeling approaches. Using the structural connectome as a framework and modeling the signal at each node with neural mass equations that combine local activity with delayed, weighted inputs from other nodes leads to a reasonable reproduction of average functional connectivity in a variety of models and parameterizations. Assessing these biophysical models through the dynamics that they create, rather than

Neural underpinnings

Especially for fMRI, the neural underpinnings of time-varying functional connectivity remain poorly understood. A burgeoning number of studies have shown that fluctuations in vigilance level are major contributors to changes in connectivity, but the extent to which more subtle changes in the brain's functional configuration can be resolved are still under investigation. Garth Thompson reviews the neural and metabolic underpinnings of the dynamics observed in resting state fMRI, key pieces that

Individual variability

An interesting question in dynamic connectivity is the extent to which certain dynamic features are common across subjects as a group. Xie et al. show that individual differences in functional connectivity that persist over a range of conditions tend to obscure the detection of cognitively-relevant states based on functional connectivity, and that minimization of the individual variability improves sensitivity to these cognitive state changes.

Individual variability in brain dynamics could arise

Method developments

One of the critical needs for effective investigation of dynamic connectivity is for improved analytical methods developed and honed specifically for the extraction of relevant dynamic parameters. A number of these methods are reviewed or presented in this special issue. One aspect of this is the development of new acquisition methods to improve the temporal and/or spatial resolution of the data. Le Van et al. review advances in fast MRI methods for dynamic imaging, while O'Neill et al. discuss

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