C
Christopher J. Honey
Researcher at Johns Hopkins University
Publications - 91
Citations - 19270
Christopher J. Honey is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Resting state fMRI & Default mode network. The author has an hindex of 42, co-authored 82 publications receiving 16508 citations. Previous affiliations of Christopher J. Honey include University of Washington & Indiana University.
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Mapping the Structural Core of Human Cerebral Cortex
Patric Hagmann,Leila Cammoun,Xavier Gigandet,Reto Meuli,Christopher J. Honey,Van J. Wedeen,Olaf Sporns +6 more
TL;DR: The spatial and topological centrality of the core within cortex suggests an important role in functional integration and a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants.
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Predicting human resting-state functional connectivity from structural connectivity
Christopher J. Honey,Olaf Sporns,Leila Cammoun,X. Gigandet,Jean-Philippe Thiran,Reto Meuli,Patric Hagmann,Patric Hagmann +7 more
TL;DR: Although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.
Predicting human resting-state functional connectivity from structural connectivity
Christopher J. Honey,Olaf Sporns,Leila Cammoun,X. Gigandet,Jean-Philippe Thiran,Reto Meuli,Patric Hagmann,Patric Hagmann +7 more
TL;DR: In this article, the authors investigate whether systems-level properties of functional networks can be explained by structural properties of the underlying anatomical network, using functional MRI and diffusionspectrum imaging tractography.
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Network structure of cerebral cortex shapes functional connectivity on multiple time scales.
TL;DR: Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, this work finds structure–function relations at multiple temporal scales.
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Identification and classification of hubs in brain networks.
TL;DR: This study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.