Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain

By Xia Liang, Qihong Zou, Yong He, Yihong Yang. Proceedings of the National Academy of Sciences of the United States of America, 2013

656 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/73acdc6070053b4e8ed287c12cd9ac5d2e497420

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. E. Bullmore
3. Yong He
4. Alan C. Evans
5. Jinhui Wang
6. D. Bassett
7. Andrew Zalesky
8. Alex Fornito

In Radiology, Nuclear Medicine and Imaging

1. Olaf Sporns
2. Yong He
3. E. Bullmore
4. Jinhui Wang
5. Alan C. Evans
6. Alex Fornito
7. Andrew Zalesky
8. D. Bassett

Citations in the corpus

• The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, background): “…shown that high degree and highly efficient hub nodes of normal functional MRI connectomes have higher blood flow, glucose metabolic rates, and longer connection distance, than non-hub nodes (van den Heuvel and Sporns, 2011; Alexander-Bloch et al., 2013; Liang et al., 2013; Tomasi et al., 2013).”
• Time-resolved resting-state brain networks (2014, background): “High-efficiency networks may be energy demanding (43, 44), as suggested by increased cerebral blood flow to the strong, long-range functional connections facilitating integration across disparate network elements (45).”
• Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments (2019, methodology): “Global brain connectivity with global signal regression (GBCr) using resting state blood oxygen level dependent (BOLD) fMRI is considered a valid marker of global intrinsic network activity that correlates with brain function and regional cerebral blood flow (Cole et al., 2012; Liang et al., 2013).”
• BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics (2013, methodology): “Since the BrainNet Viewer was released on the NITRC and SPM websites, many researchers have adopted this toolbox to visualize the characteristics and divergences of brain networks for connectome-based methodological studies [80,81] and under healthy and diseased conditions, such as age [34], gender ”
• GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “…because it tends to have higher test-retest (TRT) reliability than other nodal centrality metrics (Wang et al., 2011; Cao et al., 2014), and it is well in line with physiological measures, such as the rates of cerebral blood flow and glucose metabolism (Liang et al., 2013; Tomasi et al., 2013).”