Rewiring of neuronal networks during synaptic silencing

Abstract

Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity.

Activity Dependent and Independent Determinants of Synaptic Size Diversity

“…To examine this expectation, we raised networks of cultured rat cortical neurons from Day 1 in culture in TTX (1 M), CNQX (10 M), and APV (50 M), potent inhibitors of voltage gated sodium channels, AMPA-type and NMDA-type glutamate receptors, respectively. No overt effects on cell viability were observed, in agreement with many early studies (van Huizen et al, 1985, Ramakers et al, 1993Craig et al, 1994;Verderio et al, 1994;Benson and Cohen, 1996;Murthy et al, 2001) as well as more recent ones (Wrosch et al, 2017;Hobbiss et al, 2018).…”

Neuronal Spatial Arrangement Shapes Effective Connectivity Traits of in vitro Cortical Networks

“…In particular, the use of small living neuronal networks as laboratories for connectivity studies has gained substantial attention. Two technologies to monitor activity in these living systems have become central, namely calcium fluorescence imaging [5], [6], [7], [8], [9] and multi-electrode arrays (MEAs) [10], [11], [12], [13], [14], [15]. The interest of these studies is not only to quantify the mechanisms shaping neuron-to-neuron interactions, but also to understand up…”

Common network effect-patterns after monoamine reuptake inhibition in dissociated hippocampus cultures

“…This spike estimation yielded a binary time log for each cell with zeros (indicating no spiking activity) and ones (indicating spikes) in the different time frames. Based on these binary spiking data, we reconstructed effective neuronal networks in the cultures with a previously published machine learning model (Wrosch et al 2017). A number of methods have been proposed to infer connectivity from spiking activity (Salinas and Sejnowski 2001; Xu et al Stetter et al 2012;Wrosch et al 2017;Lungarella et al 2007;Schreiber 2000).…”

“…Networks of neurons are studied at many different levels and are presented in the mathematical form of a graph which consists of nodes (building blocks of the network-on different scales these may be single neurons, groups of neurons, or distinct brain areas) and edges (the connections linking the nodes) (Bassett and Sporns 2017;Wrosch et al 2017). Depending on the nature of relationship between the network nodes that we focus on, we can generally distinguish three types of neuronal networks: Anatomical networks represent physical connections (Bullmore and Sporns 2009), functional networks describe the statistical dependence between the activities of two nodes without specifying the cause of correlation (Bullmore and Sporns 2009;Feldt et al 2011), and finally, effective networks determine the influence that the activity of one node has on another node (Feldt et al 2011;Bullmore and Sporns 2009).…”