The human connectome: a complex network

By Olaf Sporns. Annals of the New York Academy of Sciences, 2011

1,400 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ee94504092bc3cd8ab5134c39e42e2e298cb2e8a

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Citations in the corpus

The Conscious Id (2013, background): “Rather, this is likely to be an emergent property of activity in a complex, hierarchically organized system (Deco, Rolls, & Romo, 2009; Eguiluz, Chialvo, Cecchi, Baliki, & Apkarian, 2005; Friston & Ao, 2012; Sporns, 2011; Tononi, 2008).”
The roles of the temporal lobe in creative insight: an integrated review (2017, background): “…is increasingly understood to be the outcome of flexible and distributed neural interactions (Dietrich & Kanso, 2010; Rubinov & Sporns, 2010; Sporns, 2011), there is a need for more sophisticated measures, such as cross-frequency EEG interactions, fMRI and/or EEG-derived directed graph…”
Sex differences in the structural connectome of the human brain (2013, background): “Rather than investigating individual regions or tracts in isolation, the brain can be analyzed on the whole as a large and complex network known as the human connectome (29).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “The topology and geometry of a brain network model is represented by the so-called connectome (Sporns et al., 2005; Sporns, 2011a,b, 2012, 2013) if it consists of a set of two matrices representing weights or strength of the connections and the white matter fiber lengths.”
A review of modularization techniques in artificial neural networks (2019, background): “…of relative connections between neurons and that functionality emerges from intra-modular and inter-modular interactions revolutionized the way we research nervous systems and transformed the idea of a connectome (Bullmore and Bassett, 2011; Sporns, 2011) into a key area of brain research.”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “Nodes with higher centrality in the network identify regions that have the potential to participate in a large number of functional interactions [90].”
Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis (2012, methodology): “, 2009), allowing for instance the unraveling of the functional connectivity between all possible brain regions, as is done under the Connectome project (Sporns et al., 2005; Sporns, 2011)1.”
Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence (2017, methodology): “Different indices can be calculated for a graph that describe aspects of this topology, and thereby provide information about the brain's functional organization (Rubinov & Sporns, 2010; Sporns, 2011).”
Schizophrenia, neuroimaging and connectomics (2012, background): “Small-world properties are found in a range of complex networks (Newman, 2003; Watts and Strogatz, 1998) and the combination of high Cl and low L is thought to provide the brain with an optimal structure to simultaneously support locally segregated and globally integrated processing (Bassett and Bul”
The Restless Brain (2011, neutral): “Beginning with the clarion call of Crick and Jones (1993) and stimulated by the subsequent prospect of obtaining adequate functional and anatomical data through imaging, the call for a ‘‘Human Connectome’’ was made by Sporns and colleagues (Sporns, 2011; Sporns et al., 2005).”
Network attributes for segregation and integration in the human brain. (2013, background): “This framework bridges and unifies the domains of neuroanatomy (‘structural connectivity’ [9]) and brain dynamics (‘functional and effective connectivity’ [10]) by linking neuronal operations”
Rich-Club Organization of the Human Connectome (2011, background): “At the large scale, its complex wiring diagram forms a network of hundreds of brain regions and thousands of interconnecting white matter axonal pathways (Sporns, 2011).”
Metric learning with spectral graph convolutions on brain connectivity networks (2018, background): “During the last decade, there has been increasing interest in the study of the human connectome (Sporns, 2011), which involves representing a set of brain regions along with their structural and/or functional interactions as networks.”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “Understanding the brain's behaviour and function has been a prominent and ongoing research subject for over a century (Sporns, 2011).”