Complex network theory and the brain

Papo, David; Buldú, Javier M.; Boccaletti, Stefano; Bullmore, Edward T.

doi:10.1098/rstb.2013.0520

Cited by 127 publications

(85 citation statements)

References 44 publications

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“…All other motifs comprise connections within and between modules. Rich nodes [59] typically occupy an apex position on three-node motifs (figure 2g) and mediate most minimum path length routes through the connectome [61,67,68]. We return to the role of these motifs in shaping network dynamics later.…”

Section: Dwelling In the Rich Club (A) Model Construction: Structure mentioning

confidence: 99%

Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

Gollo

Zalesky

Hutchison

et al. 2015

Phil. Trans. R. Soc. B

179

205

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One contribution of 11 to a theme issue 'Cerebral cartography: a vision of its future'. For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously-elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding 'feeder' cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.

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Section: Dwelling In the Rich Club (A) Model Construction: Structure mentioning

confidence: 99%

Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

Gollo

Zalesky

Hutchison

et al. 2015

Phil. Trans. R. Soc. B

179

205

View full text Add to dashboard Cite

show abstract

“…In terms of capacity, there are two measures of importance, the number of operations a system can perform, and the amount of information that can be stored. The number of synaptic operations per second in a human brain has been estimated to lie between 1 x 10 15 and 1 x 10 17 [5,28]. While this number is impressive, even in 2007, humanity's general purpose computers were capable of performing well over 1 x 10 18 instructions per second [5].…”

Section: Digital Storagementioning

confidence: 99%

“…While this number is impressive, even in 2007, humanity's general purpose computers were capable of performing well over 1 x 10 18 instructions per second [5]. Estimates suggest that the storage capacity of an individual human brain is about 10 12 Bytes [28,29]. On a per capita basis, this is matched by current digital storage (5 x 10 21 Bytes per 7.2 x 10 9 people).…”

Section: Digital Storagementioning

confidence: 99%

Information in the Biosphere: Biological and Digital Worlds

Gillings

Hilbert

Kemp

2016

Trends in Ecology & Evolution

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“…Yet, in a broad spectrum of settings-statistical mechanics [19,2], biology [8, 17,20], social science [14,10,21]-entities are inextricably relational by nature, fitting into an ontological paradigm intuitively composed of nodes and edges. In these cases, failures to consider the rich relational structure of data by our learning algorithms severely constrain the depth and completeness of concepts we can capture.…”

Section: Introductionmentioning

confidence: 99%

Learning Maliciousness in Cybersecurity Graphs

Connor¹,

Elisabeth²

2017

IJCSDF

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Statistical relational learning is concerned with inferring patterns from data explicitly modeled as graphs. In this work, we present an approach to learning latent topological and attribute features of multi-relational property graphs in settings where a fraction of node attributes are missing. This work draws upon prior work based on tensor factorization. We demonstrate how learned latent embeddings can be used to approximate the missing attributes. The methods explored are applied to the problem of detecting malicious entities in a novel cybersecurity ontology in which emails are explicitly modeled as graphs.

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Complex network theory and the brain

Abstract: One contribution of 12 to a Theme Issue 'Complex network theory and the brain'.

Cited by 127 publications

References 44 publications

Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

Information in the Biosphere: Biological and Digital Worlds

Learning Maliciousness in Cybersecurity Graphs

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