Multiplex core-periphery organization of the human connectome
December 23, 2017 Β· Declared Dead Β· π Journal of the Royal Society Interface
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Federico Battiston, Jeremy Guillon, Mario Chavez, Vito Latora, Fabrizio De Vico Fallani
arXiv ID
1801.01913
Category
q-bio.NC
Cross-listed
cs.SI,
physics.soc-ph
Citations
52
Venue
Journal of the Royal Society Interface
Last Checked
5 months ago
Abstract
The behavior of many complex systems is determined by a core of densely interconnected units. While many methods are available to identify the core of a network when connections between nodes are all of the same type, a principled approach to define the core when multiple types of connectivity are allowed is still lacking. Here we introduce a general framework to define and extract the core-periphery structure of multi-layer networks by explicitly taking into account the connectivity of the nodes at each layer. We show how our method works on synthetic networks with different size, density, and overlap between the cores at the different layers. We then apply the method to multiplex brain networks whose layers encode information both on the anatomical and the functional connectivity among regions of the human cortex. Results confirm the presence of the main known hubs, but also suggest the existence of novel brain core regions that have been discarded by previous analysis which focused exclusively on the structural layer. Our work is a step forward in the identification of the core of the human connectome, and contributes to shed light to a fundamental question in modern neuroscience.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.NC
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
SuperSpike: Supervised learning in multi-layer spiking neural networks
R.I.P.
π»
Ghosted
Generic decoding of seen and imagined objects using hierarchical visual features
R.I.P.
π»
Ghosted
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
R.I.P.
π»
Ghosted
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
R.I.P.
π»
Ghosted
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted