Mapping the Structural Core of Human Cerebral Cortex

By Patric Hagmann, Leila Cammoun, Xavier Gigandet, Reto Meuli, Christopher J. Honey, Van J. Wedeen, Olaf Sporns. PLoS Biology, 2008

4,324 citations recorded by the source corpus.

Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/dc707c0f126e15da2142c2ee9242559e6177e2df

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Citations in the corpus

Beyond the connectome: How neuromodulators shape neural circuits (2012, background): “brain connectivity, such as diffusion tensor imaging, also suggest a very high degree of interconnectedness between human brain regions [57].”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “We show these difficulties by investigating examples of anatomical networks in the human brain reported by Hagmann and colleagues [10].”
Resting-State Retinotopic Organization in the Absence of Retinal Input and Visual Experience (2015, background): “Slow fluctuations in the BOLD signal measured at rest show correlations across brain regions (Hagmann et al., 2008; Greicius et al., 2009; Honey et al., 2009; Bowman et al., 2012), which are thought to partially reflect neural activity (Biswal et al., 1995; Greicius et al., 2003).”
A whole brain fMRI atlas generated via spatially constrained spectral clustering (2011, methodology): “, 2002a] or random parcellation [Hagmann et al., 2008; Zalesky et al., 2010] can be achieved by setting the similarity between neighboring GM voxels to one.”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “Furthermore, the brain parcellation used to obtain ROI time series by local averaging was the one proposed by Hagmann and was composed of 66 regions (Hagmann et al., 2008).”
Development of functional and structural connectivity within the default mode network in young children (2010, methodology): “Both fiber density and mean FA have been used previously to quantify structural connectivity, particularly to characterize white matter integrity (Hagmann et al., 2008; van den Heuvel et al., 2008).”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “28 The resulting human connectome turns out to be a network organized in hierarchical modular way (Betzel et al., 2013; Breakspear, 2017; Hagmann et al., 2008; Kaiser, 2011; Meunier et al., 2010; Sporns, 2010; Sporns et al., 2004, 2005).”
The role of the posterior cingulate cortex in cognition and disease. (2014, methodology): “The structural connectivity of the PCC is closely related to its functional connectivity with other brain regions, which can be measured with functional MRI (Hagmann et al., 2008; Margulies et al., 2009; Leech et al., 2011).”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “Keywords: Resting-state, Network Model, Dynamic FC, Envelope FC Introduction Within the rapidly growing field of brain Connectomics, two main modes of connectivity emerge: Structural and Functional (Biswal et al., 1995, Sporns et al., 2005, Hagmann et al., 2008, Sporns, 2011).”
Modeling the Impact of Lesions in the Human Brain (2009, methodology): “The structural connectivity pattern used in the present model was obtained by noninvasive diffusion imaging [4].”
Complex network measures of brain connectivity: Uses and interpretations (2010, background): “, 2009) quantify brain networks with a small number of neurobiologically meaningful and easily computable measures (Sporns and Zwi, 2004; Achard et al., 2006; Bassett et al., 2006; He et al., 2007; Hagmann et al., 2008).”