Networks of the Brain

  title={Networks of the Brain},
  author={Donlin M. Long},
  journal={Neurosurgery Quarterly},
  • D. Long
  • Published 1 May 2011
  • Art
  • Neurosurgery Quarterly
Models of Network Growth All networks, whether they are social, technological, or biological, are the result of a growth process. Many of these networks continue to grow for prolonged periods of time, continually modifying their connectivity structure throughout their entire existence. For example, the World Wide Web has grown from a small number of cross-linked documents in the early 1 990s to an estimated 30 billion indexed web pages in 2009.3 The extraordinary growth of the Web continues… 

Modular Brain Networks.

A number of methods for detecting modules in both structural and functional brain networks are surveyed and their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics are considered.

Graph theory methods: applications in brain networks

  • O. Sporns
  • Computer Science
    Dialogues in clinical neuroscience
  • 2018
A brief review surveys some of the most commonly used and neurobiologically insightful graph measures and techniques, including the detection of network communities or modules, and the identification of central network elements that facilitate communication and signal transfer.

Structure and function of complex brain networks

  • O. Sporns
  • Psychology
    Dialogues in clinical neuroscience
  • 2013
Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.

Brain-like large scale cognitive networks and dynamics

This work implements finite state models on neural networks to display the outcoming brain dynamics, using different types of networks, which correspond to diverse segmentation methods and brain atlases, and observes that the behavior of these systems is completely different from random and/or artificially generated networks.

Perspective: network-guided pattern formation of neural dynamics

This work discusses the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns and proposes a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states.

The Laplacian spectrum of neural networks

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks.

Network maps of the human brain's rich club

This work investigated rich club organization in the human brain in datasets that recorded weighted projections among different anatomical regions of the cerebral cortex, recorded from several cohorts of healthy human volunteers.

Topological Comparison of Brain Functional Networks and Internet Service Providers

This paper studies certain key topological features of brain functional networks obtained from functional magnetic resonance imaging (fMRI) measurements and compares complex network measures of the extracted topologies with those from Internet service providers (ISPs).


Current empirical efforts toward generating a network map of the human brain, the human connectome, are reviewed, and how the connectome can provide new insights into the organization of the brain's structural connections and their role in shaping functional dynamics are explored.

The Role of Topology in the Synchronization of Neuronal Networks Based on the Hodgkin-Huxley Model.

This research investigates the dynamics of different networks by random rewiring of the synaptic connections by creating various topologies and reveals a decreasing trend of coherence level starting from a complete excitatory network and gradually increasing of inhibitory neurons.



Graph theoretical analysis of complex networks in the brain

These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity, and increasing evidence that various types of brain disease may be associated with deviations of the functional network topology from the optimal small- world pattern.

Emergence of scaling in random networks

A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

The Structure and Function of Complex Networks

Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Classes of network connectivity and dynamics

Different classes of network are identified, including networks that are characterized by high complexity, which have distinct structural characteristics such as clustered connectivity and short wiring length similar to those of large-scale networks of the cerebral cortex.

Criticality of spreading dynamics in hierarchical cluster networks without inhibition

Findings indicate that a hierarchical cluster architecture may provide the structural basis for the stable and diverse functional patterns observed in cortical networks.

Self-organized critical neural networks.

A mechanism for self-organization of the degree of connectivity in model neural networks is studied in a two-dimensional neural network with randomly wired asymmetric weights, which is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters.

Network structure of cerebral cortex shapes functional connectivity on multiple time scales

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.

The small world of the cerebral cortex

All cortical connection matrices examined in this study exhibit “small-world” attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements.