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Synchronous bursting events dynamics in a PKC⁻ network at a 24 DIV (NW3). (A) SBEs were initiated in a much narrower region than in PKCN networks and propagated across the network in a much more homogeneous fashion (3 × 3 median filter smoothing). White crosses mark the first ten recruited electrodes in a sample SBE and the black spot their means of the x and y coordinates defined as onset location. (B) Onset locations were located predominantly along the boundary and formed distinct BIZs (N = 486 SBE, t = 103 min; 4.7 SBE/min). Contour lines indicate the frequency with which individual electrodes were among onset electrodes (smoothed by 3 × 3 median filtering). (C) BIZs were identified by spatial centroid clustering of onset locations (cut-off at 1 mm distance between onset locations). (D) Burst initiation was dominated by even fewer BIZs than in PKCN networks. The nine most frequent BIZ are color coded. (E) Mapping individual SBE onset locations shown in B to their respective BIZ (color code as in D) indicates that dominating BIZ lay close to but not at the network boundary. (F) BIZs reflected the centers of burst onset regions, which were mostly compact but not confined to extremely localized positions. The maps show the probability by which electrodes were among the first ten onset electrodes of bursts starting within the dominating BIZs. (G) As in PKCN networks, average relative activity levels at BIZ electrodes (ratio of the mean AFR at BIZ electrodes and of all other electrodes with spike activity) were slightly above network average in the dominating BIZs and always lower than the 25% of highest AFRs. (H) Map of relative activity levels (ratio between the AFR at individual electrodes and network AFR during SBEs). BIZs appeared mostly located between hot and cold spots. Note that the large central region with high relative activity levels never initiated SBEs. (I) Median burst strength at BIZ electrodes when driving SBEs (active) or recruited during SBEs initiated by other BIZs (passive). Activity in the major BIZs was not significantly higher when they initiated SBEs. (J) Average propagation patterns elicited by the first nine BIZs revealed a homogeneous propagation of activity from different BIZ positions. (K) Similarity between propagation patterns was determined as the correlation of electrode recruitment ranks during SBEs. Sorting according to BIZ assignment revealed a very high correlation between propagation patterns originating at the same BIZs. (L) As in PKCN networks, the correlation between propagation patterns dropped as function of distance between BIZs but yielded highly anti-correlated patterns for BIZ located at opposed sites of the network. (M) The distribution of SBE onset locations for the same network recorded one week later was highly similar (30 DIV, N = 627 SBE, t = 140 min; 4.5 SBE/min). (N) The overall distribution of BIZs was mostly preserved during development. Comparing letter sequence (reflecting decreasing frequency) to the numbers in H shows, however, that their influence had changed, focussing SBE initiation even more on one dominating BIZ. (O) Relative activity levels across the MEA displayed highly similar patterns at 24 and 30 DIV.
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The mesoscale architecture of neuronal networks strongly influences the initiation of spontaneous activity and its pathways of propagation. Spontaneous activity has been studied extensively in networks of cultured cortical neurons that generate complex yet reproducible patterns of synchronous bursting events that resemble the activity dynamics in d...
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Citations
... This confers the capability of larger dynamical changes in the global activity necessary for the complex computations performed by the brain as well as a protective effect on different types of perturbations to the network (Yamamoto et al., 2018;Kaiser, 2010;Pan and Sinha, 2007). The study of these complex networks at different scales using concepts from network science and graph theory has gained increasing interest in recent years (Rubinov and Sporns, 2010;Khambhati et al., 2018), allowing for new insight into how network structure modulates activity (Massobrio et al., 2015;Okujeni and Egert, 2019a). This has revealed several topological hallmarks of these networks which are present across multiple scales, including their affinity for organizing into structures defined by a high degree of small-worldness, a hierarchical and modular organization, and a lack of characteristic scale (i.e. ...
Biological neural networks are characterized by short average path lengths, high clustering, and modular and hierarchical architectures. These complex network topologies strike a balance between local specialization and global synchronization via long-range connections, resulting in highly efficient communication. Here, we use a geometric network model with either an intermediate or a long-range connection probability to investigate the effects of wiring cost principles on network complexity for different spatial conformations. We find that both long-range and intermediate wiring probabilities only conform to small-world architectures for neurons in dense spatial clusters due to a decrease in wiring cost within clusters. Furthermore, both small-worldness and modularity were reduced in systems with long-range connections caused by a reduction in network clustering, allowing for novel insight into mechanisms underlying adaptive or maladaptive network alterations. Our findings corroborate previous work showing that both wiring probability and spatial distributions play a key role in neural network development.
... Our results confirm these findings, since the macroscopic statistics characterizing the spontaneous activity maintain their features also when the neuronal system is engineered as an assembly of interconnected neural networks (Fig. 2). In this kind of modular topology, the propagation of the bursting activity across the network is a distinctive element that confirms previous observations about the occurrence of burst initiation points and propagation 33 . In a previous study, initiation points have been ascribed to local inhomogeneities of the network 33 . ...
... In this kind of modular topology, the propagation of the bursting activity across the network is a distinctive element that confirms previous observations about the occurrence of burst initiation points and propagation 33 . In a previous study, initiation points have been ascribed to local inhomogeneities of the network 33 . We confirmed the occurrence of initiation points but also found that their spatial distribution is markedly non-random (Figs. 4 and 5). ...
Understanding the brain functioning is essential for governing brain processes with the aim of managing pathological network dysfunctions. Due to the morphological and biochemical complexity of the central nervous system, the development of general models with predictive power must start from in vitro brain network engineering. In the present work, we realized a micro-electrode array (MEA)-based in vitro brain network and studied its emerging dynamical properties. We obtained four-neuron-clusters (4N) assemblies by plating rat embryo cortical neurons on 60-electrode MEA with cross-shaped polymeric masks and compared the emerging dynamics with those of sister single networks (1N). Both 1N and 4N assemblies exhibited spontaneous electrical activity characterized by spiking and bursting signals up to global activation by means of network bursts. Data revealed distinct patterns of network activity with differences between 1 and 4N. Rhythmic network bursts and dominant initiator clusters suggested pacemaker activities in both assembly types, but the propagation of activation sequences was statistically influenced by the assembly topology. We proved that this rhythmic activity was ivabradine sensitive, suggesting the involvement of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and propagated across the real clusters of 4N, or corresponding virtual clusters of 1N, with dominant initiator clusters, and nonrandom cluster activation sequences. The occurrence of nonrandom series of identical activation sequences in 4N revealed processes possibly ascribable to neuroplasticity. Hence, our multi-network dissociated cortical assemblies suggest the relevance of pacemaker neurons as essential elements for generating brain network electrophysiological patterns; indeed, such evidence should be considered in the development of computational models for envisaging network behavior both in physiological and pathological conditions.
... A previous study discussing the mechanism of the beginning of bursts showed that the diversity of spatiotemporal patterns of bursts is regulated by AMPA receptors [22]. Other studies have claimed that several highly active neuronal populations (pacemaker neurons) lead to the emergence of bursts [49], and that competition among these neurons contributes to the richness of burst patterns [50]. It has been reported that transplanted NSCs could trigger the bursting behavior of hippocampal slices, when using optical stimulations and whole cell patchclamp technique [51]. ...
Objective. Neural stem cells (NSCs) are continuously produced throughout life in the hippocampus, which is a vital structure for learning and memory. NSCs in the brain incorporate into the functional hippocampal circuits and contribute to processing information. However, little is known about the mechanisms of NSCs’ activity in a pre-existing neuronal network. Here, we investigate the role of NSCs in the neuronal activity of a pre-existing hippocampal in vitro network grown on microelectrode arrays. Approach. We assessed the change in internal dynamics of the network by additional NSCs based on spontaneous activity. We also evaluated the networks’ ability to discriminate between different input patterns by measuring evoked activity in response to external inputs. Main results. Analysis of spontaneous activity revealed that additional NSCs prolonged network bursts with longer intervals, generated a lower number of initiating patterns, and decreased synchronization among neurons. Moreover, the network with NSCs showed higher synchronicity in close connections among neurons responding to external inputs and a larger difference in spike counts and cross-correlations during evoked response between two different inputs. Taken together, our results suggested that NSCs alter the internal dynamics of the pre-existing hippocampal network and produce more specific responses to external inputs, thus enhancing the ability of the network to differentiate two different inputs. Significance. We demonstrated that NSCs improve the ability to distinguish external inputs by modulating the internal dynamics of a pre-existing network in a hippocampal culture. Our results provide novel insights into the relationship between NSCs and learning and memory.
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Background:
Network alterations underlying neurodegenerative diseases often precede symptoms and functional deficits. Thus, their early identification is central for improved prognosis. In Huntington's disease (HD), the cortico-striatal networks, involved in motor function processing, are the most compromised neural substrate. However, whether the network alterations are intrinsic of the striatum or the cortex is not fully understood.
Results:
In order to identify early HD neural deficits, we characterized neuronal ensemble calcium activity and network topology of HD striatal and cortical cultures. We used large-scale calcium imaging combined with activity-based network inference analysis. We extracted collective activity events and inferred the topology of the neuronal network in cortical and striatal primary cultures from wild-type and R6/1 mouse model of HD. Striatal, but not cortical, HD networks displayed lower activity and a lessened ability to integrate information. GABAA receptor blockade in healthy and HD striatal cultures generated similar coordinated ensemble activity and network topology, highlighting that the excitatory component of striatal system is spared in HD. Conversely, NMDA receptor activation increased individual neuronal activity while coordinated activity became highly variable and undefined. Interestingly, by boosting NMDA activity, we rectified striatal HD network alterations.
Conclusions:
Overall, our integrative approach highlights striatal defective network integration capacity as a major contributor of basal ganglia dysfunction in HD and suggests that increased excitatory drive may serve as a potential intervention. In addition, our work provides a valuable tool to evaluate in vitro network recovery after treatment intervention in basal ganglia disorders.
Numerous studies suggest that biological neuronal networks self-organize towards a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro , which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks towards mesoscale criticality by creating a modular structure in neuronal networks.
SIGNIFICANCE STATEMENT:
Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomical signatures of such brain disorders.
Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these “learning populations” had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.
Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.
The spatial distribution of neurons and activity-dependent neurite outgrowth shape long-range interaction, recurrent local connectivity and the modularity in neuronal networks. We investigated how this mesoscale architecture develops by interaction of neurite outgrowth, cell migration and activity in cultured networks of rat cortical neurons and show that simple rules can explain variations of network modularity. In contrast to theoretical studies on activity-dependent outgrowth but consistent with predictions for modular networks, spontaneous activity and the rate of synchronized bursts increased with clustering, whereas peak firing rates in bursts increased in highly interconnected homogeneous networks. As Ca2+ influx increased exponentially with increasing network recruitment during bursts, its modulation was highly correlated to peak firing rates. During network maturation, long-term estimates of Ca2+ influx showed convergence, even for highly different mesoscale architectures, neurite extent, connectivity, modularity and average activity levels, indicating homeostatic regulation towards a common set-point of Ca2+ influx.
