Abstract
Editorial for the research topic: information-based methods for neuroimaging: analyzing structure, function and dynamics.
Highlights
This Research Topic gathers different contributions highlighting novel types of analysis and methods to deal more efficiently with neuroimaging data, simulated and real, acquired with different modalities
This was developed for multimodal integration of the resting state fMRI and the whole brain connectome and is based on the evidence that the level of restingstate functional correlation between any two regions decreases as the graph distance of the corresponding structural connectivity matrix between them increases
In Lee et al (2013) the Approximate Entropy (ApEn), a measure known to correlate with the level of brain consciousness, is used to characterize EEG signals in children and adults to show that the amount of ApEn is lower in children and that it correlates, in children as in adults, with consciousness; in particular, the authors show that ApEn decreases across the transition from awake to REM sleep to non-REM sleep
Summary
This Research Topic gathers different contributions highlighting novel types of analysis and methods to deal more efficiently with neuroimaging data, simulated and real, acquired with different modalities. The authors show that dynamical models aimed at reproducing the functional connectivity patterns observed in the resting brain exhibit a much better performance when they are tuned around a balanced state favoring the shifting between attractors. This was developed for multimodal integration of the resting state fMRI (rsfMRI) and the whole brain (tractography-derived) connectome and is based on the evidence that the level of restingstate functional correlation between any two regions (in general) decreases as the graph distance of the corresponding structural connectivity matrix between them increases.
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