Emergence of modular structure in a large-scale brain network with interactions between dynamics and connectivity

Abstract

A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an electroencephalography/magnetoencephalography like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i) synchronization dependent plasticity (SDP) and (ii) growth dependent plasticity (GDP). In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.

Large-scale functional network reorganization in 22q11.2 deletion syndrome revealed by modularity analysis

“…The presence of abnormal pruning is also supported by DTI studies showing that white matter abnormalities in 22q11DS are likely related to axonal damage (Jalbrzikowski et al, 2014;Kikinis et al, 2012). However, according to computational models, pruning plays a role in shaping modular communities (Stam, Hillebrand, Wang, & Van Mieghem, 2010;Vertes et al, 2012) by favoring connections between distant but functionally Fig. 3 e Average modularity partition for patients with 22q11DS and healthy controls in the whole sample, the child/ adolescent group and the adult group.…”

Dissecting functional connectivity of neuronal microcircuits: experimental and theoretical insights

“…Network growth from a theoretical perspective From a theoretical point of view, the process of network growth has always been a very useful model for understanding the emergence of connectivity as well as the general principles and constraints that govern the network structure-function relationship in stable circuits [54,55,60,110,111]. In this way, it was proposed that scalefree networks would self-assemble following a 'preferential attachment rule' [60]: scale-free networks arise by the sequential addition of new nodes, and each new coming node has a higher probability to link to highly connected nodes (i.e.…”

“…Therefore, theoretical analysis of neuronal network topology can provide many interesting clues for the cellular mechanisms by which connections might be establishing during development. Interestingly, model predictions have also started taking into account the activity of individual nodes in network growth processes [111]. This is essential given that functional connectivity develops in parallel with structural connectivity during brain maturation.…”

Go with the flow: Use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics

“…In another study based upon the same model emergence of modular structure could be explained by the interaction between the influence of distance and synchronization on connection strength . Macroscopic studies of brain networks have also revealed the impact of local structural brain lesions on network architecture (Alstott et al, 2009;Honey and Sporns, 2008;Kaiser et al, 2007;Stam et al, 2010).…”