Network structure of cerebral cortex shapes functional connectivity on multiple time scales

By Christopher J. Honey, Rolf Kötter, Michael Breakspear, Olaf Sporns. 2007

1,775 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Global and Planetary Change

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Olaf Sporns
2. D. Bassett
3. E. Bullmore
4. Yong He
5. Xi-Nian Zuo
6. Gustavo Deco
7. Vinod Menon
8. Martijn P. van den Heuvel

In Global and Planetary Change

1. Olaf Sporns
2. Karl J. Friston
3. Gustavo Deco
4. Michael Breakspear
5. Viktor Jirsa
6. Stephen M. Smith
7. Morten L. Kringelbach
8. Cornelis J. Stam

Citations in the corpus

• Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, methodology): “Several studies have successfully done so for both rs-MRI (Deco and Jirsa, 2012; Honey et al., 2007) and rs-MEG (Cabral et al., 2014b), and has even been used to model important features of sleep (Deco et al., 2013a) (see Figure 5).”
• Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations (2013, background): “In the study of Honey et al. (2007), RSNs also result from instability due to chaotic fluctuations.”
• Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “…2005; Liley and Bojak, 2005; Foster et al., 2008; Spiegler and Jirsa, 2013; Liley and Walsh, 2013) to a whole cortical area (Ghosh et al., 2008a; Deco et al., 2009; Knock et al., 2009; Victor et al., 2011; Nakagawa et al., 2013; Deco et al., 2013; Honey et al., 2007, 2009; Cabral et al., 2012).”
• Beyond Feeling: Chronic Pain Hurts the Brain, Disrupting the Default-Mode Network Dynamics (2008, result): “…because they emerge from mutual interactions among all the members of the DMN. Finally, an additional layer of complexity is added to these interactions because they seem to be frequency dependent, as suggested by recent modeling (Honey et al., 2007) and experimental (Mantini et al., 2007) results.”
• Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Dynamical models, involving the simulation of biophysically plausible neural mass models on empirically derived anatomical connectivity architectures have also proved useful for understanding the relationship between connectome structure and function in health (Breakspear et al., 2010; Cabral et al.”
• Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire (2008, methodology): “[20] substitute for the time delays, but rather introduces another component to a network’s node dynamics which we did not address.”
• Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks (2009, positive): “At the global level of human fi ber tract connectivity between brain regions, for example, edge densities around 46% can be reached (Honey et al., 2007).”