Abstract
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.
Highlights
The network structure/topology of the brain plays an indisputable role in a wide variety of tasks the brain performs (Sporns, 2010)
All networks with bimodal degree distribution have normalized rich club coefficients >1 for many club sizes, which means that the nodes with larger degrees are connected to each other more in these networks compared to random networks
Our results show the vulnerability of random networks to synaptic loss, compared to networks with bimodal degree distribution
Summary
The network structure/topology of the brain plays an indisputable role in a wide variety of tasks the brain performs (Sporns, 2010). Mesoscale and macroscale studies in humans (van den Heuvel and Sporns, 2011), mice (Oh et al, 2014) and cats (de Reus and van den Heuvel, 2013) have shown similar findings. Such heterogeneity in the network structure has been observed in cultured cells, and emergence of small-world networks (Downes et al, 2012) or rich clubs (Schroeter et al, 2015) has been reported for in vitro experiments
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