Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study

By Jinhui Wang, Liang Wang, Yufeng Zang, Hong Yang, Hehan Tang, Qiyong Gong, Zhang John Chen, Chaozhe Zhu, Yong He. Human Brain Mapping, 2009

651 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9fdc2566c2603b1cd9f78421fadcd593fdfb407e

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. Yong He
4. E. Bullmore
5. Jinhui Wang
6. Xi-Nian Zuo
7. Alan C. Evans
8. Andrew Zalesky

In Statistical and Nonlinear Physics

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

Citations in the corpus

• Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “An alternative but less common approach to define a threshold is to fix the network’s edge density (also referred to as wiring cost ), i.e. number of existing edges divided by the number of possible edges [41].”
• Multi-scale brain networks (2016, neutral): “One of the findings that has come from a detailed comparison of spatial scales is that the choice of parcellation will tend to have implications for the network's topology (Wang et al., 2009; Zalesky et al., 2010).”
• Whole-brain anatomical networks: Does the choice of nodes matter? (2010, methodology): “…90 AAL Eguiluz et al., 2005 a 140,000 Voxel-based Van den Heuvel et al., 2008 10,000 Voxel-based Liu et al., 2008 90 AAL Salvador et al., 2005c 90 AAL Salvador et al., 200b 90 AAL Wang et al., 2009a 90 vs. 70 AAL vs. ANIMAL d-MRI Gong et al., 2009 78 AAL Hagmann et al., 2007 500–4000 Gry/Wht. Surf.”
• Conserved and variable architecture of human white matter connectivity (2011, background): “The combination of these two non-random properties, known as smallworldness, is robust to atlas choice in functional brain networks (Wang et al., 2009) and robust to spatial resolution in both anatomical and functional networks (Zalesky et al.”
• Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, background): “[121] have specifically addressed this question studying the impact of two parcellation strategies on the topological organization of the brain functional network.”
• Brain graphs: graphical models of the human brain connectome. (2011, background): “There is also a rapidly growing literature on specialist applications to human neuroimaging and other neurophysiological data (for recent reviews, see Bassett & Bullmore 2006, 2009; Bullmore et al. 2009; Bullmore & Sporns 2009; Hagmann et al. 2010; and Wang et al. 2009).”
• Complex network measures of brain connectivity: Uses and interpretations (2010, background): “Networks constructed using distinct parcellation schemes may signi fi cantly differ in their properties (Wang et al., 2009a) and cannot, in general, be quantitatively compared.”
• Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans (2009, methodology): “and truncated power-law degree distribution [23,24,27].”
• A whole brain fMRI atlas generated via spatially constrained spectral clustering (2011, methodology): “[Shehzad et al., 2009; Wang et al., 2008]), their suitability for resting state FC analysis has yet to be determined.”