The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core
By Gustavo Deco, Morten L. Kringelbach, Viktor Jirsa, Petra Ritter. Scientific Reports, 2016
508 citations recorded by the source corpus.
In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of brain function.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/284d914977609a090569f1b7d0f552e93d39de5d
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In Cognitive Neuroscience
- Matthew F. Glasser
- Alan Anticevic
- D. V. Van Essen
- Stamatios N. Sotiropoulos
- John D. Murray
- J. Burt
- Markus Helmer
- Jie Lisa Ji
In Experimental and Cognitive Psychology
Citations in the corpus
- Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, result): “Computational studies of large-scale cortical circuits have shown that the structure-function relationship improves when neural dynamics are near the edge of instability (Deco et al., 2013; 2014b; 2017), which was also confirmed in our study.”