Cognitive fitness of cost-efficient brain functional networks

By D. Bassett, E. Bullmore, Andreas Meyer-Lindenberg, Jose Apud, Daniel R. Weinberger, Richard Coppola. Proceedings of the National Academy of Sciences of the United States of America, 2009

431 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/079f025c042a70292b2b48ce81677712231d3839

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In Cognitive Neuroscience

Citations in the corpus

Rich-Club Organization of the Human Connectome (2011, background): “Examining the function and role of these hubs is of special interest as they play a central role in establishing and maintaining efficient global brain communication, a crucial feature for healthy brain functioning (Bassett et al., 2009; van den Heuvel et al., 2009a).”
Schizophrenia, neuroimaging and connectomics (2012, background): “Notably, reductions in functional brain network cost-efficiency have been found using magnetoencephalography in people with schizophrenia performing a working memory task (Bassett et al., 2009).”
Function in the Human Connectome: Task-fMRI and Individual Differences in Behavior (2013, background): “033 disorders, and there is increasing evidence that this variability is associated with alterations in cognitive and behavioral variables that constrain real world function (Bassett et al., 2009; Song et al., 2008; van den Heuvel et al., 2009).”
Small-World Brain Networks Revisited (2016, background): “But what have we learnt more speciﬁ-cally about brain small-worldness since 2006, and what do we still have to learn?”
Disrupted Modularity and Local Connectivity of Brain Functional Networks in Childhood-Onset Schizophrenia (2010, neutral): “Indeed while decreased clustering in schizophrenia has been replicated in other studies, the story for global efficiency is less clear (Liu et al., 2008; Bassett et al., 2009).”
Abnormal rich club organization and functional brain dynamics in schizophrenia. (2013, background): “d Parental educational level: principal data set: ranging from (unfinished) primary school education (1) to university (7); replication data set: ranging from no education (0) to university (8).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Despite this ambiguity, path length-based measures have shown replicated associations with cognitive performance (Bassett et al., 2009; Li et al., 2009; van denHeuvel et al., 2009; Zalesky et al., 2011), strong heritability (Fornito et al.”
Experimental and theoretical approaches to conscious processing. (2011, methodology): “The networks thus identified converge well with those extracted by fMRI intercorrelation patterns during the resting state or by phase synchrony in the beta band during either working memory (Bassett et al., 2009) or attentional blink (Gross et al.”
Uncovering space-independent communities in spatial networks (2010, background): “Results are summarized in Table 1 and clearly show that the original data is signiﬁcantly more modular than networks where the weights are randomized.”
Altered Resting State Complexity in Schizophrenia (2011, background): “, 2010; Rubinov and Sporns, 2009) and less cost-efficient (Bassett et al., 2009) compared to controls.”
High-cost, high-capacity backbone for global brain communication (2012, background): “Our findings support the idea that neural architecture is organized to achieve a high level of global information integration along short communication paths (14, 15), while at the same time conserving material resources such as metabolic energy and wiring volume (22–25).”
Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, background): “The topological cost efficiency (Latora and Marchiori, 2001) of brain functional networks oscillating at -band frequencies was shown to be more strongly correlated with variability of performance on a working memory task than the cost efficiency of networks operating at other frequencies (Bassett et”
Network-based statistic: Identifying differences in brain networks (2010, background): “…MRI, electroencephalography or magnetoencephalography, temporal correlations (Achard et al., 2006; Deuker et al., 2009; Fair et al., 2009; Hayasaka & Laurienti, 2010; Honey et al., 2009; Li et al., 2009) or mutual information (Bassett et al., 2009) in the node-averaged signals are computed.”