Conserved and variable architecture of human white matter connectivity

By D. Bassett, Jesse A. Brown, Vibhas Deshpande, Jean M. Carlson, Scott T. Grafton. NeuroImage, 2011

374 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7f0e8aadaf1c713752bca49dcd800f202eb20ff2

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. Richard F. Betzel
7. Jinhui Wang
8. Martijn P. van den Heuvel

In Radiology, Nuclear Medicine and Imaging

1. Olaf Sporns
2. D. Bassett
3. Yong He
4. E. Bullmore
5. Jinhui Wang
6. Xi-Nian Zuo
7. Martijn P. van den Heuvel
8. Richard F. Betzel

Citations in the corpus

• Network attributes for segregation and integration in the human brain. (2013, background): “Using one or several of these metrics, several recent studies of networks derived from diffusion imaging and tractography [43,44,45 ] have identified specific cortical regions as putative hubs, with some of them aggregated into a highly resilient structural core [46].”
• Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, background): “atlas or parcellation scheme is the topic of several recent studies in structural [57,60], resting-state [58], and task-based [59] network architecture.”
• Structural connectomics in brain diseases (2013, positive): “These factors have been shown to significantly impact the derived structural connectomes architecture (Bassett et al., 2011; Bastiani et al., 2012; Wang et al., 2012a).”
• Altered Structural Brain Connectivity in Healthy Carriers of the Autism Risk Gene, CNTNAP2 (2011, neutral): “Even so, there is ongoing work in the field aiming at optimizing the cortical parcellation for network analyses, and at understanding how different parcellation schemes may influence different kinds of network measures (Bassett et al., 2011; Zalesky et al., 2010).”
• The human connectome: Origins and challenges (2013, background): “…topology are often seen in studies of human whole-brain structural connectivity, a systematic assessment of how much of the observed variance is due to noisy acquisitions, registration errors, or instability in reconstruction algorithms has yet to be performed (see e.g. Bassett et al., 2010).”
• Closures and Cavities in the Human Connectome (2016, background): “More precisely, the normalized edge weight Ai,j between nodes i and j is streamline countij/(volumeivolumej) 1/2 [1].”
• Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, methodology): “[11] demonstrated high reproducibility and low variability of graph metrics for both DTI and diffusion spectrum imaging data derived networks.”
• Dynamic reconfiguration of human brain networks during learning (2010, background): “More pertinent to this paper, recent evidence suggests that modular organization over several spatial scales, or hierarchical modularity, also characterizes the large-scale anatomical connectivity of the human brain (27, 28), as well as the spontaneous fluctuations (35, 36) thought to stem from anat”
• Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, background): “DSI appears to yield more accurate and robust connectivity properties than DTI (Bassett et al., 2011).”
• Rich-Club Organization of the Human Connectome (2011, background): “As mentioned, it has been noted that “graph resolution” can have an effect on the topological properties of the brain network (Wang et al., 2009; Fornito et al., 2010; Zalesky et al., 2010; Bassett et al., 2011, Wig et al., 2011).”
• Altered Resting State Complexity in Schizophrenia (2011, background): “The strength of node i is defined as the mean value of the i column of the connectivity matrix and indicates the average connectivity for a single brain region to all others (Bassett et al., 2010; Lynall et al., 2010).”
• Controllability of structural brain networks (2014, neutral): “While this potential bias does not appear to drastically alter large-scale topological properties of anatomical networks, its local effects are not well characterized 52 .”
• Contributions and challenges for network models in cognitive neuroscience (2014, background): “Indeed, it appears that many qualitative characteristics of network structure are relatively stable across different parcellations 24 .”