Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Investigating the reliability of the evoked response in human iPSCs-derived neuronal networks coupled to micro-electrode arrays

In vitro models of neuronal networks have emerged as a potent instrument for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human-induced pluripotent stem cells (hiPSCs) with micro-electrode arrays offers a means to replicate and dissect both the structural and functional elements of the human brain within a controlled in vitro environment. Given that neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation stands as a mean to comprehensively interrogate neuronal assemblies, to better understand their inherent electrophysiological dynamics. However, the establishment of standardized stimulation protocols for cultures derived from hiPSCs is still lacking, thereby hindering the precise delineation of efficacious parameters to elicit responses. To fill this gap, the primary objective of this study resides in delineating effective parameters for the electrical stimulation of hiPSCs-derived neuronal networks, encompassing the determination of voltage amplitude and stimulation frequency able to evoke reliable and stable responses. This study represents a stepping-stone in the exploration of efficacious stimulation parameters, thus broadening the electrophysiological activity profiling of neural networks sourced from human-induced pluripotent stem cells.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

Impact of Magnetite Nanowires on In Vitro Hippocampal Neural Networks

Nanomaterials design, synthesis, and characterization are ever-expanding approaches toward developing biodevices or neural interfaces to treat neurological diseases. The ability of nanomaterials features to tune neuronal networks' morphology or functionality is still under study. In this work, we unveil how interfacing mammalian brain cultured neurons and iron oxide nanowires' (NWs) orientation affect neuronal and glial densities and network activity. Iron oxide NWs were synthesized by electrodeposition, fixing the diameter to 100 nm and the length to 1 µm. Scanning electron microscopy, Raman, and contact angle measurements were performed to characterize the NWs' morphology, chemical composition, and hydrophilicity. Hippocampal cultures were seeded on NWs devices, and after 14 days, the cell morphology was studied by immunocytochemistry and confocal microscopy. Live calcium imaging was performed to study neuronal activity. Using random nanowires (R-NWs), higher neuronal and glial cell densities were obtained compared with the control and vertical nanowires (V-NWs), while using V-NWs, more stellate glial cells were found. R-NWs produced a reduction in neuronal activity, while V-NWs increased the neuronal network activity, possibly due to a higher neuronal maturity and a lower number of GABAergic neurons, respectively. These results highlight the potential of NWs manipulations to design ad hoc regenerative interfaces.

Dynamic and Functional Alterations of Neuronal Networks In Vitro upon Physical Damage: A Proof of Concept

There is a growing technological interest in combining biological neuronal networks with electronic ones, specifically for biological computation, human-machine interfacing and robotic implants. A major challenge for the development of these technologies is the resilience of the biological networks to physical damage, for instance, when used in harsh environments. To tackle this question, here, we investigated the dynamic and functional alterations of rodent cortical networks grown in vitro that were physically damaged, either by sequentially removing groups of neurons that were central for information flow or by applying an incision that cut the network in half. In both cases, we observed a remarkable capacity of the neuronal cultures to cope with damage, maintaining their activity and even reestablishing lost communication pathways. We also observed-particularly for the cultures cut in half-that a reservoir of healthy neurons surrounding the damaged region could boost resilience by providing stimulation and a communication bridge across disconnected areas. Our results show the remarkable capacity of neuronal cultures to sustain and recover from damage, and may be inspirational for the development of future hybrid biological-electronic systems.

Rich dynamics and functional organization on topographically designed neuronal networks in vitro

Neuronal cultures are a prominent experimental tool to understand complex functional organization in neuronal assemblies. However, neurons grown on flat surfaces exhibit a strongly coherent bursting behavior with limited functionality. To approach the functional richness of naturally formed neuronal circuits, here we studied neuronal networks grown on polydimethylsiloxane (PDMS) topographical patterns shaped as either parallel tracks or square valleys. We followed the evolution of spontaneous activity in these cultures along 20 days in vitro using fluorescence calcium imaging. The networks were characterized by rich spatiotemporal activity patterns that comprised from small regions of the culture to its whole extent. Effective connectivity analysis revealed the emergence of spatially compact functional modules that were associated with both the underpinned topographical features and predominant spatiotemporal activity fronts. Our results show the capacity of spatial constraints to mold activity and functional organization, bringing new opportunities to comprehend the structure-function relationship in living neuronal circuits.

Intravital calcium imaging in myeloid leukocytes identifies calcium frequency spectra as indicators of functional states

The assessment of leukocyte activation in vivo is mainly based on surrogate parameters, such as cell shape changes and migration patterns. Consequently, additional parameters are required to dissect the complex spatiotemporal activation of leukocytes during inflammation. Here, we showed that intravital microscopy of myeloid leukocyte Ca²⁺ signals with Ca²⁺ reporter mouse strains combined with bioinformatic signal analysis provided a tool to assess their activation in vivo. We demonstrated by two-photon microscopy that tissue-resident macrophages reacted to sterile inflammation in the cremaster muscle with Ca²⁺ transients in a distinct spatiotemporal pattern. Moreover, through high-resolution, intravital spinning disk confocal microscopy, we identified the intracellular Ca²⁺ signaling patterns of neutrophils during the migration cascade in vivo. These patterns were modulated by the Ca²⁺ channel Orai1 and Gα_i-coupled GPCRs, whose effects were evident through analysis of the range of frequencies of the Ca²⁺ signal (frequency spectra), which provided insights into the complex patterns of leukocyte Ca²⁺ oscillations. Together, these findings establish Ca²⁺ frequency spectra as an additional dimension to assess leukocyte activation and migration during inflammation in vivo.

High-Aspect-Ratio Nanoelectrodes Enable Long-Term Recordings of Neuronal Signals with Subthreshold Resolution

The further development of neurochips requires high-density and high-resolution recordings that also allow neuronal signals to be observed over a long period of time. Expanding fields of network neuroscience and neuromorphic engineering demand the multiparallel and direct estimations of synaptic weights, and the key objective is to construct a device that also records subthreshold events. Recently, 3D nanostructures with a high aspect ratio have become a particularly suitable interface between neurons and electronic devices, since the excellent mechanical coupling to the neuronal cell membrane allows very high signal-to-noise ratio recordings. In the light of an increasing demand for a stable, noninvasive and long-term recording at subthreshold resolution, a combination of vertical nanostraws with nanocavities is presented. These structures provide a spontaneous tight coupling with rat cortical neurons, resulting in high amplitude sensitivity and postsynaptic resolution capability, as directly confirmed by combined patch-clamp and microelectrode array measurements.

Mesmerize is a dynamically adaptable user-friendly analysis platform for 2D and 3D calcium imaging data

Calcium imaging is an increasingly valuable technique for understanding neural circuits, neuroethology, and cellular mechanisms. The analysis of calcium imaging data presents challenges in image processing, data organization, analysis, and accessibility. Tools have been created to address these problems independently, however a comprehensive user-friendly package does not exist. Here we present Mesmerize, an efficient, expandable and user-friendly analysis platform, which uses a Findable, Accessible, Interoperable and Reproducible (FAIR) system to encapsulate the entire analysis process, from raw data to interactive visualizations for publication. Mesmerize provides a user-friendly graphical interface to state-of-the-art analysis methods for signal extraction & downstream analysis. We demonstrate the broad scientific scope of Mesmerize's applications by analyzing neuronal datasets from mouse and a volumetric zebrafish dataset. We also applied contemporary time-series analysis techniques to analyze a novel dataset comprising neuronal, epidermal, and migratory mesenchymal cells of the protochordate Ciona intestinalis.

Inferring Excitatory and Inhibitory Connections in Neuronal Networks

The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.

Parkinson's disease patient-specific neuronal networks carrying the LRRK2 G2019S mutation unveil early functional alterations that predate neurodegeneration

A deeper understanding of early disease mechanisms occurring in Parkinson’s disease (PD) is needed to reveal restorative targets. Here we report that human induced pluripotent stem cell (iPSC)-derived dopaminergic neurons (DAn) obtained from healthy individuals or patients harboring LRRK2 PD-causing mutation can create highly complex networks with evident signs of functional maturation over time. Compared to control neuronal networks, LRRK2 PD patients’ networks displayed an elevated bursting behavior, in the absence of neurodegeneration. By combining functional calcium imaging, biophysical modeling, and DAn-lineage tracing, we found a decrease in DAn neurite density that triggered overall functional alterations in PD neuronal networks. Our data implicate early dysfunction as a prime focus that may contribute to the initiation of downstream degenerative pathways preceding DAn loss in PD, highlighting a potential window of opportunity for pre-symptomatic assessment of chronic degenerative diseases.

Neuronal Graphs: A Graph Theory Primer for Microscopic, Functional Networks of Neurons Recorded by Calcium Imaging

Connected networks are a fundamental structure of neurobiology. Understanding these networks will help us elucidate the neural mechanisms of computation. Mathematically speaking these networks are "graphs"-structures containing objects that are connected. In neuroscience, the objects could be regions of the brain, e.g., fMRI data, or be individual neurons, e.g., calcium imaging with fluorescence microscopy. The formal study of graphs, graph theory, can provide neuroscientists with a large bank of algorithms for exploring networks. Graph theory has already been applied in a variety of ways to fMRI data but, more recently, has begun to be applied at the scales of neurons, e.g., from functional calcium imaging. In this primer we explain the basics of graph theory and relate them to features of microscopic functional networks of neurons from calcium imaging-neuronal graphs. We explore recent examples of graph theory applied to calcium imaging and we highlight some areas where researchers new to the field could go awry.

Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers

The interplay between excitation and inhibition is crucial for neuronal circuitry in the brain. Inhibitory cell fractions in the neocortex and hippocampus are typically maintained at 15 to 30%, which is assumed to be important for stable dynamics. We have studied systematically the role of precisely controlled excitatory/inhibitory (E/I) cellular ratios on network activity using mice hippocampal cultures. Surprisingly, networks with varying E/I ratios maintain stable bursting dynamics. Interburst intervals remain constant for most ratios, except in the extremes of 0 to 10% and 90 to 100% inhibitory cells. Single-cell recordings and modeling suggest that networks adapt to chronic alterations of E/I compositions by balancing E/I connectivity. Gradual blockade of inhibition substantiates the agreement between the model and experiment and defines its limits. Combining measurements of population and single-cell activity with theoretical modeling, we provide a clearer picture of how E/I balance is preserved and where it fails in living neuronal networks.

Functional strengthening through synaptic scaling upon connectivity disruption in neuronal cultures

An elusive phenomenon in network neuroscience is the extent of neuronal activity remodeling upon damage. Here, we investigate the action of gradual synaptic blockade on the effective connectivity in cortical networks in vitro. We use two neuronal cultures configurations-one formed by about 130 neuronal aggregates and another one formed by about 600 individual neurons-and monitor their spontaneous activity upon progressive weakening of excitatory connectivity. We report that the effective connectivity in all cultures exhibits a first phase of transient strengthening followed by a second phase of steady deterioration. We quantify these phases by measuring GEFF, the global efficiency in processing network information. We term hyperefficiency the sudden strengthening of GEFF upon network deterioration, which increases by 20-50% depending on culture type. Relying on numerical simulations we reveal the role of synaptic scaling, an activity-dependent mechanism for synaptic plasticity, in counteracting the perturbative action, neatly reproducing the observed hyperefficiency. Our results demonstrate the importance of synaptic scaling as resilience mechanism.

3D Free-Standing Ordered Graphene Network Geometrically Regulates Neuronal Growth and Network Formation

The control of cell-microenvironment interactions plays a pivotal role in constructing specific scaffolds for tissue engineering. Here, we fabricated a 3D free-standing ordered graphene (3D-OG) network with a precisely defined pattern. When primary cortical cells are cultured on 3D-OG scaffolds, they form well-defined 3D connections. Astrocytes have a more ramified shape similar to that seen in vivo because of the nanosized ripples and wrinkles on the surface of graphene skeleton. Neurons have axons and dendrites aligned along the graphene skeleton, allowing the formation of neuronal networks with highly controlled connections. Neuronal networks have higher electrical activity with functional signaling over a long distance along the graphene skeleton. Our study, for the first time, investigated the geometrical cues on ordered neuronal growth and network formation with the support of graphene in 3D, which therefore advanced the development of customized scaffolds for brain-machine interfaces or neuroprosthetic devices.

Low-cost calcium fluorometry for long-term nanoparticle studies in living cells

Calcium fluorometry is critical to determine cell homeostasis or to reveal communication patterns in neuronal networks. Recently, characterizing calcium signalling in neurons related to interactions with nanomaterials has become of interest due to its therapeutic potential. However, imaging of neuronal cell activity under stable physiological conditions can be either very expensive or limited in its long-term capability. Here, we present a low-cost, portable imaging system for long-term, fast-scale calcium fluorometry in neurons. Using the imaging system, we revealed temperature-dependent changes in long-term calcium signalling in kidney cells and primary cortical neurons. Furthermore, we introduce fast-scale monitoring of synchronous calcium activity in neuronal cultures in response to nanomaterials. Through graph network analysis, we found that calcium dynamics in neurons are temperature-dependent when exposed to chitosan-coated nanoparticles. These results give new insights into nanomaterial-interaction in living cultures and tissues based on calcium fluorometry and graph network analysis.

Deficits in coordinated neuronal activity and network topology are striatal hallmarks in Huntington's disease

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. GABA_A 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.

Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences

Propagation of signals between neurons and brain regions provides information about the functional properties of neural networks, and thus information transfer. Advances in optical imaging and statistical analyses of acquired optical signals have yielded various metrics for inferring neural connectivity, and hence for mapping signal intercorrelation. However, a single coefficient is traditionally derived to classify the connection strength between two cells, ignoring the fact that neural systems are inherently time-variant systems. To overcome these limitations, we utilized a time-varying Pearson's correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12-15 hippocampal neurons from GCaMP6s mice after applying various concentrations of glutamate. Results provide a comprehensive overview of resulting firing patterns, network connectivity, signal directionality, and network properties. Together, these metrics provide a more comprehensive and robust method of analyzing transient neural signals, and enable future investigations for tracking the effects of different stimuli on network properties.

Chemically Cross-Linked Carbon Nanotube Films Engineered to Control Neuronal Signaling

In recent years, the use of free-standing carbon nanotube (CNT) films for neural tissue engineering has attracted tremendous attention. CNT films show large surface area and high electrical conductivity that combined with flexibility and biocompatibility may promote neuron growth and differentiation while stimulating neural activity. In addition, adhesion, survival, and growth of neurons can be modulated through chemical modification of CNTs. Axonal and synaptic signaling can also be positively tuned by these materials. Here we describe the ability of free-standing CNT films to influence neuronal activity. We demonstrate that the degree of cross-linking between the CNTs has a strong impact on the electrical conductivity of the substrate, which, in turn, regulates neural circuit outputs.

Impact of targeted attack on the spontaneous activity in spatial and biologically-inspired neuronal networks

We study the structural and dynamical consequences of damage in spatial neuronal networks. Inspired by real in vitro networks, we construct directed networks embedded in a two-dimensional space and follow biological rules for designing the wiring of the system. As a result, synthetic cultures display strong metric correlations similar to those observed in real experiments. In its turn, neuronal dynamics is incorporated through the Izhikevich model adopting the parameters derived from observation in real cultures. We consider two scenarios for damage, targeted attacks on those neurons with the highest out-degree and random failures. By analyzing the evolution of both the giant connected component and the dynamical patterns of the neurons as nodes are removed, we observe that network activity halts for a removal of 50% of the nodes in targeted attacks, much lower than the 70% node removal required in the case of random failures. Notably, the decrease of neuronal activity is not gradual. Both damage scenarios portray "boosts" of activity just before full silencing that are not present in equivalent random (Erdös-Rényi) graphs. These boosts correspond to small, spatially compact subnetworks that are able to maintain high levels of activity. Since these subnetworks are absent in the equivalent random graphs, we hypothesize that metric correlations facilitate the existence of local circuits sufficiently integrated to maintain activity, shaping an intrinsic mechanism for resilience.

Hydrogel Skin-Covered Neurons Self-Assembled with Gustatory Cells for Selective Taste Stimulation

Many technical challenges exist in the co-culture of multiple types of cells, including medium optimization, cell-to-cell connection, and selective data acquisition of cellular responses. Particularly, mixed cellular responses limit the precise interpretation of intercellular signal transduction. Here, we report the formation of an agarose gel skin on neurons closely assembled with gustatory cells to selectively stimulate gustatory cells by retarding the diffusion of tastants to neurons. The signal transmission, triggered by denatonium benzoate, from gustatory cells to neurons was monitored using intracellular calcium ion concentrations. The agarose gel skin efficiently suppressed the direct transfer of tastants to neurons, decreasing the number of responsive neurons from 56 to 13% and the number of calcium ion signals per neuron from multiple to single. The assembly of neurons with gustatory cells induced the high level of neuronal responses through taste signal transduction from gustatory cells to neurons. However, the calcium ion signal peaks of free neurons coated with agarose gel were much shorter and weaker than those of neurons closely assembled with gustatory cells. This work demonstrated that agarose gel skin is a simple, fast, and effective means to increase the signal selectivity of cellular responses in the co-culture of multiple types of cells.

Pannexin 1 Regulates Network Ensembles and Dendritic Spine Development in Cortical Neurons

Dendritic spines are the postsynaptic targets of excitatory synaptic inputs that undergo extensive proliferation and maturation during the first postnatal month in mice. However, our understanding of the molecular mechanisms that regulate spines during this critical period is limited. Previous work has shown that pannexin 1 (Panx1) regulates neurite growth and synaptic plasticity. We therefore investigated the impact of global Panx1 KO on spontaneous cortical neuron activity using Ca²⁺ imaging and in silico network analysis. Panx1 KO increased both the number and size of spontaneous co-active cortical neuron network ensembles. To understand the basis for these findings, we investigated Panx1 expression in postnatal synaptosome preparations from early postnatal mouse cortex. Between 2 and 4 postnatal weeks, we observed a precipitous drop in cortical synaptosome protein levels of Panx1, suggesting it regulates synapse proliferation and/or maturation. At the same time points, we observed significant enrichment of the excitatory postsynaptic density proteins PSD-95, GluA1, and GluN2a in cortical synaptosomes from global Panx1 knock-out mice. Ex vivo analysis of pyramidal neuron structure in somatosensory cortex revealed a consistent increase in dendritic spine densities in both male and female Panx1 KO mice. Similar findings were observed in an excitatory neuron-specific Panx1 KO line (Emx1-Cre driven; Panx1 cKO^E) and in primary Panx1 KO cortical neurons cultured in vitro. Altogether, our study suggests that Panx1 negatively regulates cortical dendritic spine development.

Calcium Activity Dynamics Correlate with Neuronal Phenotype at a Single Cell Level and in a Threshold-Dependent Manner

Calcium is a ubiquitous signaling molecule that plays a vital role in many physiological processes. Recent work has shown that calcium activity is especially critical in vertebrate neural development. Here, we investigated if calcium activity and neuronal phenotype are correlated only on a population level or on the level of single cells. Using Xenopus primary cell culture in which individual cells can be unambiguously identified and associated with a molecular phenotype, we correlated calcium activity with neuronal phenotype on the single-cell level. This analysis revealed that, at the neural plate stage, a high frequency of low-amplitude spiking activity correlates with an excitatory, glutamatergic phenotype, while high-amplitude spiking activity correlates with an inhibitory, GABAergic phenotype. Surprisingly, we also found that high-frequency, low-amplitude spiking activity correlates with neural progenitor cells and that differentiating cells exhibit higher spike amplitude. Additional methods of analysis suggested that differentiating marker tubb2b-expressing cells exhibit relatively persistent and predictable calcium activity compared to the irregular activity of neural progenitor cells. Our study highlights the value of using a range of thresholds for analyzing calcium activity data and underscores the importance of employing multiple methods to characterize the often irregular, complex patterns of calcium activity during early neural development.

Carbon Nanotubes, Directly Grown on Supporting Surfaces, Improve Neuronal Activity in Hippocampal Neuronal Networks

Carbon nanotube (CNT)-modified surfaces unequivocally demonstrate their biocompatibility and ability to boost the electrical activity of neuronal cells cultured on them. Reasons for this effect are still under debate. However, the intimate contact at the membrane level between these thready nanostructures and cells, in combination with their unique electrical properties, seems to play an important role. The entire existing literature exploiting the effect of CNTs on modulating cellular behavior deals with cell cultures grown on purified multiwalled carbon nanotubes (MWNTs) deposited on a supporting surface via drop-casting or mechanical entrapment. Here, for the first time, it is demonstrated that CNTs directly grown on a supporting silicon surface by a chemical vapor deposition (CVD)-assisted technique have the same effect. It is shown that primary neuronal cells developed above a carpet of CVD CNTs form a healthy and functional network. The resulting neuronal network shows increased electrical activity when compared to a similar network developed on a control glass surface. The low cost and high versatility of the here presented CVD-based synthesis process, together with the possibility to create on supporting substrate patterns of any arbitrary shape of CNTs, open up new opportunities for brain-machine interfaces or neuroprosthetic devices.

Reconstruction of Functional Connectivity from Multielectrode Recordings and Calcium Imaging

In the last two decades, increasing research efforts in neuroscience have been focused on determining both structural and functional connectivity of brain circuits, with the main goal of relating the wiring diagram of neuronal systems to their emerging properties, from the microscale to the macroscale. While combining multisite parallel recordings with structural circuits' reconstruction in vivo is still very challenging, the reductionist in vitro approach based on neuronal cultures offers lower technical difficulties and is much more stable under control conditions. In this chapter, we present different approaches to infer the connectivity of cultured neuronal networks using multielectrode array or calcium imaging recordings. We first formally introduce the used methods, and then we will describe into details how those methods were applied in case studies. Since multielectrode array and calcium imaging recordings provide distinct and complementary spatiotemporal features of neuronal activity, in this chapter we present the strategies implemented with the two different methodologies in distinct sections.

Assessing functional connectivity across 3D tissue engineered axonal tracts using calcium fluorescence imaging

OBJECTIVE

Micro-tissue engineered neural networks (micro-TENNs) are anatomically-inspired constructs designed to structurally and functionally emulate white matter pathways in the brain. These 3D neural networks feature long axonal tracts spanning discrete neuronal populations contained within a tubular hydrogel, and are being developed to reconstruct damaged axonal pathways in the brain as well as to serve as physiologically-relevant in vitro experimental platforms. The goal of the current study was to characterize the functional properties of these neuronal and axonal networks.

APPROACH

Bidirectional micro-TENNs were transduced to express genetically-encoded calcium indicators, and spontaneous fluorescence activity was recorded using real-time microscopy at 20 Hz from specific regions-of-interest in the neuronal populations. Network activity patterns and functional connectivity across the axonal tracts were then assessed using various techniques from statistics and information theory including Pearson cross-correlation, phase synchronization matrices, power spectral analysis, directed transfer function, and transfer entropy.

MAIN RESULTS

Pearson cross-correlation, phase synchronization matrices, and power spectral analysis revealed high values of correlation and synchronicity between the spatially segregated neuronal clusters connected by axonal tracts. Specifically, phase synchronization revealed high synchronicity of  >0.8 between micro-TENN regions of interest. Normalized directed transfer function and transfer entropy matrices suggested robust information flow between the neuronal populations. Time varying power spectrum analysis revealed the strength of information propagation at various frequencies. Signal power strength was visible at elevated peak levels for dominant delta (1-4 Hz) and theta (4-8 Hz) frequency bands and progressively weakened at higher frequencies. These signal power strength results closely matched normalized directed transfer function analysis where near synchronous information flow was detected between frequencies of 2-5 Hz.

SIGNIFICANCE

To our knowledge, this is the first report using directed transfer function and transfer entropy methods based on fluorescent calcium activity to estimate functional connectivity of distinct neuronal populations via long-projecting, 3D axonal tracts in vitro. These functional data will further improve the design and optimization of implantable neural networks that could ultimately be deployed to reconstruct the nervous system to treat neurological disease and injury.

In vivo imaging of neuronal calcium during electrode implantation: Spatial and temporal mapping of damage and recovery

Implantable electrode devices enable long-term electrophysiological recordings for brain-machine interfaces and basic neuroscience research. Implantation of these devices, however, leads to neuronal damage and progressive neural degeneration that can lead to device failure. The present study uses in vivo two-photon microscopy to study the calcium activity and morphology of neurons before, during, and one month after electrode implantation to determine how implantation trauma injures neurons. We show that implantation leads to prolonged, elevated calcium levels in neurons within 150 μm of the electrode interface. These neurons show signs of mechanical distortion and mechanoporation after implantation, suggesting that calcium influx is related to mechanical trauma. Further, calcium-laden neurites develop signs of axonal injury at 1-3 h post-insert. Over the first month after implantation, physiological neuronal calcium activity increases, suggesting that neurons may be recovering. By defining the mechanisms of neuron damage after electrode implantation, our results suggest new directions for therapies to improve electrode longevity.

Fibroblast growth factor 2 regulates activity and gene expression of human post-mitotic excitatory neurons

Many neuropsychiatric disorders are thought to result from subtle changes in neural circuit formation. We used human embryonic stem cells and induced pluripotent stem cells (hiPSCs) to model mature, post-mitotic excitatory neurons and examine effects of fibroblast growth factor 2 (FGF2). FGF2 gene expression is known to be altered in brain regions of major depressive disorder (MDD) patients and FGF2 has anti-depressive effects in animal models of depression. We generated stable inducible neurons (siNeurons) conditionally expressing human neurogenin-2 (NEUROG2) to generate a homogenous population of post-mitotic excitatory neurons and study the functional as well as the transcriptional effects of FGF2. Upon induction of NEUROG2 with doxycycline, the vast majority of cells are post-mitotic, and the gene expression profile recapitulates that of excitatory neurons within 6 days. Using hES cell lines that inducibly express NEUROG2 as well as GCaMP6f, we were able to characterize spontaneous calcium activity in these neurons and show that calcium transients increase in the presence of FGF2. The FGF2-responsive genes were determined by RNA-Seq. FGF2-regulated genes previously identified in non-neuronal cell types were up-regulated (EGR1, ETV4, SPRY4, and DUSP6) as a result of chronic FGF2 treatment of siNeurons. Novel neuron-specific genes were also identified that may mediate FGF2-dependent increases in synaptic efficacy including NRXN3, SYT2, and GALR1. Since several of these genes have been implicated in MDD previously, these results will provide the basis for more mechanistic studies of the role of FGF2 in MDD.

N-Cadherin modified lipid bilayers promote neural network formation and circuitry

Neural adhesion, maturation, and the correct wiring of the brain to establish each neuron's intended connectivity are controlled by complex interactions of bioactive molecules such as ligands, growth factors, or enzymes. The correct pairing of adjacent neurons is thought to be highly regulated by ligand-mediated cell-cell adhesion proteins, which are known to induce signaling activities. We developed a new platform consisting of supported lipid bilayers incorporated with Fc-chimera synaptic proteins like ephrinA5 or N-cadherin. We extensively characterized their function employing a quartz crystal microbalance with dissipation (QCM-D), calcium imaging, and immunofluorescence analysis. Our biomimetic platform has been shown to promote neural cell adhesion and to improve neural maturation at day in vitro 7 (DIV7) as indicated by an elevated expression of synaptophysin.

Graphene Improves the Biocompatibility of Polyacrylamide Hydrogels: 3D Polymeric Scaffolds for Neuronal Growth

In tissue engineering strategies, the design of scaffolds based on nanostructures is a subject undergoing intense research: nanomaterials may affect the scaffolds properties, including their ability to interact with cells favouring cell growth and improving tissue performance. Hydrogels are synthetic materials widely used to obtain realistic tissue constructs, as they resemble living tissues. Here, different hydrogels with varying content of graphene, are synthesised by in situ radical polymerization of acrylamide in aqueous graphene dispersions. Hydrogels are characterised focusing on the contribution of the nanomaterial to the polymer network. Our results suggest that graphene is not a mere embedded nanomaterial within the hydrogels, rather it represents an intrinsic component of these networks, with a specific role in the emergence of these structures. Moreover, a hybrid hydrogel with a graphene concentration of only 0.2 mg mL-1 is used to support the growth of cultured brain cells and the development of synaptic activity, in view of exploiting these novel materials to engineer the neural interface of brain devices of the future. The main conclusion of this work is that graphene plays an important role in improving the biocompatibility of polyacrylamide hydrogels, allowing neuronal adhesion.

Inflammatory cytokine-induced changes in neural network activity measured by waveform analysis of high-content calcium imaging in murine cortical neurons

During acute neuroinflammation, increased levels of cytokines within the brain may contribute to synaptic reorganization that results in long-term changes in network hyperexcitability. Indeed, inflammatory cytokines are implicated in synaptic dysfunction in epilepsy and in an array of degenerative and autoimmune diseases of the central nervous system. Current tools for studying the impact of inflammatory factors on neural networks are either insufficiently fast and sensitive or require complicated and costly experimental rigs. Calcium imaging offers a reasonable surrogate for direct measurement of neuronal network activity, but traditional imaging paradigms are confounded by cellular heterogeneity and cannot readily distinguish between glial and neuronal calcium transients. While the establishment of pure neuron cultures is possible, the removal of glial cells ignores physiologically relevant cell-cell interactions that may be critical for circuit level disruptions induced by inflammatory factors. To overcome these issues, we provide techniques and algorithms for image processing and waveform feature extraction using automated analysis of spontaneous and evoked calcium transients in primary murine cortical neuron cultures transduced with an adeno-associated viral vector driving the GCaMP6f reporter behind a synapsin promoter. Using this system, we provide evidence of network perturbations induced by the inflammatory cytokines TNFα, IL1β, and IFNγ.

Mapping functional connectivity of bursting neuronal networks

Using single-cell laser scanning photostimulation (LSPS) combined with broad-field calcium imaging, we measured the functional connectivity of neuronal cultures before and after the developmental appearance of network bursting. From these data, network properties were determined for these relatively large neuronal networks. Based on these properties, we found that although 'small-world' network behavior existed throughout this time period, only average node degree and global efficiency correlate with the development of network bursting while clustering and local efficiency remained relatively constant.

Dominance of Metric Correlations in Two-Dimensional Neuronal Cultures Described through a Random Field Ising Model

We introduce a novel random field Ising model, grounded on experimental observations, to assess the importance of metric correlations in cortical circuits in vitro. Metric correlations arise from both the finite axonal length and the heterogeneity in the spatial arrangement of neurons. The experiments consider the response of neuronal cultures to an external electric stimulation for a gradually weaker connectivity strength between neurons, and in cultures with different spatial configurations. The model can be analytically solved in the metric-free, mean-field scenario. The presence of metric correlations precipitates a strong deviation from the mean field. Null models of the same networks that preserve the distribution of connections recover the mean field. Our results show that metric-inherited correlations in spatial networks dominate the connectivity blueprint, mask the actual distribution of connections, and may emerge as the asset that shapes network dynamics.

Size-dependent regulation of synchronized activity in living neuronal networks

We study the effect of network size on synchronized activity in living neuronal networks. Dissociated cortical neurons form synaptic connections in culture and generate synchronized spontaneous activity within 10 days in vitro. Using micropatterned surfaces to extrinsically control the size of neuronal networks, we show that synchronized activity can emerge in a network as small as 12 cells. Furthermore, a detailed comparison of small (∼20 cells), medium (∼100 cells), and large (∼400 cells) networks reveal that synchronized activity becomes destabilized in the small networks. A computational modeling of neural activity is then employed to explore the underlying mechanism responsible for the size effect. We find that the generation and maintenance of the synchronized activity can be minimally described by: (1) the stochastic firing of each neuron in the network, (2) enhancement in the network activity in a positive feedback loop of excitatory synapses, and (3) Ca-dependent suppression of bursting activity. The model further shows that the decrease in total synaptic input to a neuron that drives the positive feedback amplification of correlated activity is a key factor underlying the destabilization of synchrony in smaller networks. Spontaneous neural activity plays a critical role in cortical information processing, and our work constructively clarifies an aspect of the structural basis behind this.

Graphene Oxide Nanosheets Reshape Synaptic Function in Cultured Brain Networks

Graphene offers promising advantages for biomedical applications. However, adoption of graphene technology in biomedicine also poses important challenges in terms of understanding cell responses, cellular uptake, or the intracellular fate of soluble graphene derivatives. In the biological microenvironment, graphene nanosheets might interact with exposed cellular and subcellular structures, resulting in unexpected regulation of sophisticated biological signaling. More broadly, biomedical devices based on the design of these 2D planar nanostructures for interventions in the central nervous system require an accurate understanding of their interactions with the neuronal milieu. Here, we describe the ability of graphene oxide nanosheets to down-regulate neuronal signaling without affecting cell viability.

Magnetite-Amyloid-β deteriorates activity and functional organization in an in vitro model for Alzheimer's disease

The understanding of the key mechanisms behind human brain deterioration in Alzheimer' disease (AD) is a highly active field of research. The most widespread hypothesis considers a cascade of events initiated by amyloid-β peptide fibrils that ultimately lead to the formation of the lethal amyloid plaques. Recent studies have shown that other agents, in particular magnetite, can also play a pivotal role. To shed light on the action of magnetite and amyloid-β in the deterioration of neuronal circuits, we investigated their capacity to alter spontaneous activity patterns in cultured neuronal networks. Using a versatile experimental platform that allows the parallel monitoring of several cultures, the activity in controls was compared with the one in cultures dosed with magnetite, amyloid-β and magnetite-amyloid-β complex. A prominent degradation in spontaneous activity was observed solely when amyloid-β and magnetite acted together. Our work suggests that magnetite nanoparticles have a more prominent role in AD than previously thought, and may bring new insights in the understanding of the damaging action of magnetite-amyloid-β complex. Our experimental system also offers new interesting perspectives to explore key biochemical players in neurological disorders through a controlled, model system manner.

From 2D to 3D: novel nanostructured scaffolds to investigate signalling in reconstructed neuronal networks

To recreate in vitro 3D neuronal circuits will ultimately increase the relevance of results from cultured to whole-brain networks and will promote enabling technologies for neuro-engineering applications. Here we fabricate novel elastomeric scaffolds able to instruct 3D growth of living primary neurons. Such systems allow investigating the emerging activity, in terms of calcium signals, of small clusters of neurons as a function of the interplay between the 2D or 3D architectures and network dynamics. We report the ability of 3D geometry to improve functional organization and synchronization in small neuronal assemblies. We propose a mathematical modelling of network dynamics that supports such a result. Entrapping carbon nanotubes in the scaffolds remarkably boosted synaptic activity, thus allowing for the first time to exploit nanomaterial/cell interfacing in 3D growth support. Our 3D system represents a simple and reliable construct, able to improve the complexity of current tissue culture models.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

BACKGROUND

Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s-1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking.

NEW METHOD

Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge.

RESULTS

We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau.

COMPARISON WITH EXISTING METHOD(S)

We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods.

CONCLUSIONS

We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease.

Emergence of assortative mixing between clusters of cultured neurons

The analysis of the activity of neuronal cultures is considered to be a good proxy of the functional connectivity of in vivo neuronal tissues. Thus, the functional complex network inferred from activity patterns is a promising way to unravel the interplay between structure and functionality of neuronal systems. Here, we monitor the spontaneous self-sustained dynamics in neuronal cultures formed by interconnected aggregates of neurons (clusters). Dynamics is characterized by the fast activation of groups of clusters in sequences termed bursts. The analysis of the time delays between clusters' activations within the bursts allows the reconstruction of the directed functional connectivity of the network. We propose a method to statistically infer this connectivity and analyze the resulting properties of the associated complex networks. Surprisingly enough, in contrast to what has been reported for many biological networks, the clustered neuronal cultures present assortative mixing connectivity values, meaning that there is a preference for clusters to link to other clusters that share similar functional connectivity, as well as a rich-club core, which shapes a 'connectivity backbone' in the network. These results point out that the grouping of neurons and the assortative connectivity between clusters are intrinsic survival mechanisms of the culture.

Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging

Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.