Contributions and challenges for network models in cognitive neuroscience

By Olaf Sporns. Nature Neuroscience, 2014

743 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2c31d790da1e1e3f75197f214f03abc6ee62d80c

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “…approaches have opened new avenues toward studying the neural mechanisms underlying human cognition and behavior from the view of interregional brain interactions (Park and Friston, 2013; Pessoa, 2014; Sporns, 2014; Medaglia et al., 2015; Petersen and Sporns, 2015; Kriegeskorte and Douglas, 2018).”
Precision functional mapping of individual human brains (2017, positive): “This effect converges with theoretical predictions that noise may subtly bias network measures (Sporns, 2014).”
Mapping the human brain’s cortical-subcortical functional network organization (2018, positive): “…have proven to be remarkably valuable in elucidating functional brain organization and have provided an initial framework for functional network analyses in a variety of studies, both in health and disease, yielding important new insights in multiple fields of neuroscience (Sporns, 2014).”
A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex (2015, neutral): “We then investigate the implications of local circuit heterogeneity for macroscopic dynamics measured by functional connectivity (i.e. correlations in activity across areas).”
Information Processing in the Mental Workspace Is Fundamentally Distributed (2016, background): “Mounting empirical evidence derived from new, network- and information-based analytical techniques paints a more complex picture of high-level cognitive processing, suggesting that it may, in many cases, occur at a level of organization that transcends any single neural structure (Sporns, 2014; Turk”
Communication dynamics in complex brain networks (2017, background): “Understanding how brain function emerges from patterns of communication between large numbers of neuronal elements stands as one of the enduring challenges of modern neuroscience 1–3 .”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, neutral): “(Sporns, 2014).”
Cognitive Network Neuroscience (2015, background): “A prevailing challenge for network neuroscience is to provide the basis for formal integration with established cognitive theories (Sporns, 2014).”
Graph theory methods: applications in brain networks (2018, background): “organization into distinct network communities or modules 6,10,23 ( Figure 1 ).”
Population-Averaged Atlas of the Macroscale Human Structural Connectome and Its Network Topology (2018, neutral): “The organization of the structural connections in the human brain determines how neural networks communicate, thereby serving as a critical constraint on brain functionality and providing potential etiology for clinical pathology (Bota et al., 2015; Sporns, 2014).”
Two’s company, three (or more) is a simplex (2016, background): “Their use has led to the development of novel diagnostic biomarkers (Stam, 2014) and conceptual cognitive frameworks (Sporns, 2014) that illustrate a paradigm shift in systems, cognitive, and clinical neuroscience: namely, that brain function and alteration are inherently networked phenomena.”
The challenge of mapping the human connectome based on diffusion tractography (2017, neutral): “Potential advances achieved in tractography will have an important impact on graph-analytical studies of the structural connectome 2, 67 .”
Generative models of the human connectome (2015, neutral): “In this format, the connectome can be analyzed using tools from network science and graph theory (Bullmore and Sporns, 2009; Sporns, 2014).”