Age-related changes in modular organization of human brain functional networks

By David Meunier, Sophie Achard, Alexa M. Morcom, E. Bullmore. NeuroImage, 2009

772 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/664967b5c542489881d7e48784a4b732ceee5e98

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. Alan C. Evans
6. Xi-Nian Zuo
7. Richard F. Betzel
8. Jinhui Wang

In Radiology, Nuclear Medicine and Imaging

1. Olaf Sporns
2. D. Bassett
3. Yong He
4. E. Bullmore
5. Alan C. Evans
6. Jinhui Wang
7. Xi-Nian Zuo
8. Richard F. Betzel

Citations in the corpus

• A Brain-Wide Study of Age-Related Changes in Functional Connectivity. (2015, background): “Modularity on the other hand was reported to be similar in older and younger participants (Meunier et al. 2009).”
• Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, methodology): “This property of hierarchical modularity can be discerned simply by inspection of the coclassification matrix of each network (Figure 2 left) but is more robustly demonstrated by the results of iterative modular decomposition using the Louvain algorithm [18,19] (Figure 2 right).”
• Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “In addition to development-related changes, some groups have also investigated the effects of normal aging on the functional organization of large-scale brain networks during rest (Achard et al., 2007; Meunier et al., 2009a).”
• Brain graphs: graphical models of the human brain connectome. (2011, background): “Imaging studies of brain systems or networks inevitably depend on some measure of signal association or covariation between regions, but the neurobiological basis is not yet settled for either interregional correlations in brain structure estimated over subjects in MRI or interregional correlations…”
• Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, methodology): “Here the field moves mostly by consensus, with around twenty components being used, though recent experiments have used fifty or more [35, 36].”
• Closures and Cavities in the Human Connectome (2016, background): “[43] D Meunier, S Achard, A Morcom, and E Bullmore.”
• Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “Thus, the small-world architecture of a brain functional network is closely related to its modularity 72 .”
• Can structure predict function in the human brain? (2010, neutral): “…(Reid et al., 2009), informational efficiency (Achard and Bullmore, 2007), small-world connectivity (Sporns and Zwi, 2004; Bassett and Bullmore, 2006), modularity (Chen et al., 2008; Meunier et al., 2009), and hub structure (Hagmann et al., 2008; Buckner et al., 2009) in brain network data.”
• Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory (2009, background): “showed that human functional brain networks appear to be modular, and that a large frontal module has extensive connections with other brain areas [30].”