Astrocytes facilitate gabazine-evoked electrophysiological hyperactivity and distinct biochemical responses in mature neuronal cultures.

Gamma-aminobutyric acid (GABA) is the principal inhibitory neurotransmitter in the adult brain that binds to GABA receptors and hyperpolarizes the postsynaptic neuron. Gabazine acts as a competitive antagonist to type A GABA receptors (GABAAR), thereby causing diminished neuronal hyperpolarization and GABAAR-mediated inhibition. However, the biochemical effects and the potential regulatory role of astrocytes in this process remain poorly understood. To address this, we investigated the neuronal responses of gabazine in rat cortical cultures containing varying ratios of neurons and astrocytes. Electrophysiological characterization was performed utilizing microelectrode arrays (MEAs) with topologically controlled microcircuit cultures that enabled control of neuronal network growth. Biochemical analysis of the cultures was performed using traditional dissociated cultures on coverslips. Our study indicates that, upon gabazine stimulation, astrocyte-rich neuronal cultures exhibit elevated electrophysiological activity and tyrosine phosphorylation of tropomyosin receptor kinase B (TrkB; receptor for brain-derived neurotrophic factor), along with distinct cytokine secretion profiles. Notably, neurons lacking proper astrocytic support were found to experience synapse loss and decreased mitogen-activated protein kinase (MAPK) phosphorylation. Furthermore, astrocytes contributed to neuronal viability, morphology, vascular endothelial growth factor (VEGF) secretion, and overall neuronal network functionality, highlighting the multifunctional role of astrocytes.

Astrocytes Exhibit a Protective Role in Neuronal Firing Patterns under Chemically Induced Seizures in Neuron-Astrocyte Co-Cultures.

Astrocytes and neurons respond to each other by releasing transmitters, such as γ-aminobutyric acid (GABA) and glutamate, that modulate the synaptic transmission and electrochemical behavior of both cell types. Astrocytes also maintain neuronal homeostasis by clearing neurotransmitters from the extracellular space. These astrocytic actions are altered in diseases involving malfunction of neurons, e.g., in epilepsy, Alzheimer's disease, and Parkinson's disease. Convulsant drugs such as 4-aminopyridine (4-AP) and gabazine are commonly used to study epilepsy in vitro. In this study, we aim to assess the modulatory roles of astrocytes during epileptic-like conditions and in compensating drug-elicited hyperactivity. We plated rat cortical neurons and astrocytes with different ratios on microelectrode arrays, induced seizures with 4-AP and gabazine, and recorded the evoked neuronal activity. Our results indicated that astrocytes effectively counteracted the effect of 4-AP during stimulation. Gabazine, instead, induced neuronal hyperactivity and synchronicity in all cultures. Furthermore, our results showed that the response time to the drugs increased with an increasing number of astrocytes in the co-cultures. To the best of our knowledge, our study is the first that shows the critical modulatory role of astrocytes in 4-AP and gabazine-induced discharges and highlights the importance of considering different proportions of cells in the cultures.

Lead field theory provides a powerful tool for designing microelectrode array impedance measurements for biological cell detection and observation.

BACKGROUND: Our aim is to introduce a method to enhance the design process of microelectrode array (MEA) based electric bioimpedance measurement systems for improved detection and viability assessment of living cells and tissues. We propose the application of electromagnetic lead field theory and reciprocity for MEA design and measurement result interpretation. Further, we simulated impedance spectroscopy (IS) with two- and four-electrode setups and a biological cell to illustrate the tool in the assessment of the capabilities of given MEA electrode constellations for detecting cells on or in the vicinity of the microelectrodes. RESULTS: The results show the power of the lead field theory in electromagnetic simulations of cell-microelectrode systems depicting the fundamental differences of two- and four-electrode IS measurement configurations to detect cells. Accordingly, the use in MEA system design is demonstrated by assessing the differences between the two- and four-electrode IS configurations. Further, our results show how cells affect the lead fields in these MEA system, and how we can utilize the differences of the two- and four-electrode setups in cell detection. The COMSOL simulator model is provided freely in public domain as open source. CONCLUSIONS: Lead field theory can be successfully applied in MEA design for the IS based assessment of biological cells providing the necessary visualization and insight for MEA design. The proposed method is expected to enhance the design and usability of automated cell and tissue manipulation systems required for bioreactors, which are intended for the automated production of cell and tissue grafts for medical purposes. MEA systems are also intended for toxicology to assess the effects of chemicals on living cells. Our results demonstrate that lead field concept is expected to enhance also the development of such methods and devices.

Electric field temporal interference stimulation of neurons in vitro.

Electrical stimulation (ES) techniques, such as deep brain and transcranial electrical stimulation, have shown promise in alleviating the symptoms of depression and other neurological disorders in vivo. A new noninvasive ES method called temporal interference stimulation (TIS), possesses great potential as it can be used to steer the stimulation and possibly selectively modulate different brain regions. To study TIS in a controlled environment, we successfully established an in vitro 'TIS on a chip' setup using rat cortical neurons on microelectrode arrays (MEAs) in combination with a current stimulator. We validated the developed TIS system and demonstrated the spatial steerability of the stimulation by direct electric field measurements in the chip setup. We stimulated cultures of rat cortical neurons at 28 days in vitro (DIV) by two-channel stimulation delivering 1) TIS at 653 Hz and 643 Hz, resulting in a 10 Hz frequency envelope, 2) low-frequency stimulation (LFS) at 10 Hz and 3) high-frequency stimulation (HFS) at 653 Hz. Unstimulated cultures were used as control/sham. We observed the differences in the electric field strengths during TIS, HFS, and LFS. Moreover, HFS and LFS had the smallest effects on neuronal activity. Instead, TIS elicited neuronal electrophysiological responses, especially 24 hours after stimulation. Our 'TIS on a chip' approach eludicates the applicability of TIS as a method to modulate neuronal electrophysiological activity. The TIS on a chip approach provides spatially steerable stimuli while mitigating the effects of high stimulus fields near the stimulation electrodes. Thus, the approach opens new avenues for stimulation on a chip applications, allowing the study of neuronal responses to gain insights into the potential clinical applications of TIS in treating various brain disorders.

Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review.

Biological neuronal cells communicate using neurochemistry and electrical signals. The same phenomena also allow us to probe and manipulate neuronal systems and communicate with them. Neuronal system malfunctions cause a multitude of symptoms and functional deficiencies that can be assessed and sometimes alleviated by electrical stimulation. Our working hypothesis is that real-time closed-loop full-duplex measurement and stimulation paradigms can provide more in-depth insight into neuronal networks and enhance our capability to control diseases of the nervous system. In this study, we review extracellular electrical stimulation methods used in in vivo, in vitro, and in silico neuroscience research and in the clinic (excluding methods mainly aimed at neuronal growth and other similar effects) and highlight the potential of closed-loop measurement and stimulation systems. A multitude of electrical stimulation and measurement-based methods are widely used in research and the clinic. Closed-loop methods have been proposed, and some are used in the clinic. However, closed-loop systems utilizing more complex measurement analysis and adaptive stimulation systems, such as artificial intelligence systems connected to biological neuronal systems, do not yet exist. Our review promotes the research and development of intelligent paradigms aimed at meaningful communications between neuronal and information and communications technology systems, "dialogical paradigms," which have the potential to take neuroscience and clinical methods to a new level.

Corrigendum: Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements.

[This corrects the article DOI: 10.3389/fncom.2016.00112.].

Advances in Human Stem Cell-Derived Neuronal Cell Culturing and Analysis.

This chapter provides an overview of the current stage of human in vitro functional neuronal cultures, their biological application areas, and modalities to analyze their behavior. During the last 10 years, this research area has changed from being practically non-existent to one that is facing high expectations. Here, we present a case study as a comprehensive short history of this process based on extensive studies conducted at NeuroGroup (University of Tampere) and Computational Biophysics and Imaging Group (Tampere University of Technology), ranging from the differentiation and culturing of human pluripotent stem cell (hPSC)-derived neuronal networks to their electrophysiological analysis. After an introduction to neuronal differentiation in hPSCs, we review our work on their functionality and approaches for extending cultures from 2D to 3D systems. Thereafter, we discuss our target applications in neuronal developmental modeling, toxicology, drug screening, and disease modeling. The development of signal analysis methods was required due to the unique functional and developmental properties of hPSC-derived neuronal cells and networks, which separate them from their much-used rodent counterparts. Accordingly, a line of microelectrode array (MEA) signal analysis methods was developed. This work included the development of action potential spike detection methods, entropy-based methods and additional methods for burst detection and quantification, joint analysis of spikes and bursts to analyze the spike waveform compositions of bursts, assessment methods for network synchronization, and computational simulations of synapses and neuronal networks.

On electrophysiological signal complexity during biological neuronal network development and maturation.

Developing neuronal populations are assumed to increase their synaptic interactions and generate synchronized activity, such as bursting, during maturation. These effects may arise from increasing interactions of neuronal populations and increasing simultaneous intra-population activity in developing networks. In this paper, we investigated the neuronal network activity and its complexity by means of self-similarity during neuronal network development. We studied the phenomena using computational neuronal network models and actual in vitro microelectrode array data measured from a developing neuronal network of dissociated mouse cortical neurons. To achieve this, we assessed the spiking and bursting characteristics of the networks, and computed the signal complexity with Sample Entropy. The results show that we can relate increasing simultaneous activity in a neuronal population with decreasing entropy, and track the network development and maturation using this. We can conclude that the complexity of neuronal network signals decreases during the maturation. This can emerge from the fact that as networks mature, they exhibit more synchronous activity, thus decreasing the complexity of its signaling. However, increasing the number of interacting populations has lesser effect on the signal complexity. The entropy based measure provides a tool to assess the complexity of the neuronal network activity, and can be useful in the assessment of developing networks or the effects of drugs and toxins on their functioning.

Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony.

In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relations with in vivo rat hippocampal recordings, and observe the time courses of the correlations between different regions of hippocampus in three sequential recordings. Additionally, we evaluate the results with a commonly employed causality analysis method to assess the possible correlated findings. Results show that time correlated spectral entropy reveals different levels of interrelations in neuronal networks, which can be interpreted as different levels of neuronal network synchrony.

Joint analysis of extracellular spike waveforms and neuronal network bursts.

BACKGROUND: Neuronal networks are routinely assessed based on extracellular electrophysiological microelectrode array (MEA) measurements by spike sorting, and spike and burst statistics. We propose to jointly analyze sorted spikes and detected bursts, and hypothesize that the obtained spike type compositions of the bursts can provide new information on the functional networks. NEW METHOD: Spikes are detected and sorted to obtain spike types and bursts are detected. In the proposed joint analysis, each burst spike is associated with a spike type, and the spike type compositions of the bursts are assessed. RESULTS: The proposed method was tested with simulations and MEA measurements of in vitro human stem cell derived neuronal networks under different pharmacological treatments. The results show that the treatments altered the spike type compositions of the bursts. For example, 6-cyano-7-nitroquinoxaline-2,3-dione almost completely abolished two types of spikes which had composed the bursts in the baseline, while bursts of spikes of two other types appeared more frequently. This phenomenon was not observable by spike sorting or burst analysis alone, but was revealed by the proposed joint analysis. COMPARISON WITH EXISTING METHODS: The existing methods do not provide the information obtainable with the proposed method: for the first time, the spike type compositions of bursts are analyzed. CONCLUSIONS: We showed that the proposed method provides useful and novel information, including the possible changes in the spike type compositions of the bursts due to external factors. Our method can be employed on any data exhibiting sortable action potential waveforms and detectable bursts.

Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements.

Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the synchronizations. With the real in vitro MEA data, CorSE produced biologically plausible results. Since CorSE analyses continuous data, it is not affected by possibly poor spike or other event detection quality. We conclude that CorSE can reveal neuronal network synchronization based on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.

Independent component analysis of neural populations from multielectrode field potential measurements.

Independent component analysis (ICA) is proposed for analysis of neural population activity from multichannel electrophysiological field potential measurements. The proposed analysis method provides information on spatial extents of active neural populations, locations of the populations with respect to each other, population evolution, including merging and splitting of populations in time, and on time lag differences between the populations. In some cases, results of the proposed analysis may also be interpreted as independent information flows carried by neurons and neural populations. In this paper, a detailed description of the analysis method is given. The proposed analysis is demonstrated with an illustrative simulation, and with an exemplary analysis of an in vivo multichannel recording from rat hippocampus. The proposed method can be applied in analysis of any recordings of neural networks in which contributions from a number of neural populations or information flows are simultaneously recorded via a number of measurement points, as well in vivo as in vitro.

A fast stimulability screening protocol for neuronal cultures on microelectrode arrays.

Microelectrode arrays (MEAs) are used to study the electrical activity in brain slices and neuronal cultures. MEA experiments for the analysis of electrical stimulation responses require the tissue or culture to be prone to stimulation. For brain slices, potential stimulation sites may be directly visible in microscope, in which case the determination of stimulability at those locations is sufficient. In unstructured neuronal cultures, potential stimulation sites may not be known a priori, and spatial stimulability screening should be performed. Considering, e.g., 59 microelectrode sites, each to be stimulated several times, may result in long screening times, unacceptable with a MEA system without an integrated CO2 incubator, or in high stimulation effects on the networks. Here, we describe an implementation of a fast stimulation protocol employing pseudorandom stimulation site switching aiming at alleviating the network effects of the stimulability screening. In this paper, we show the usability of the proposed protocol by first detecting stimulable locations and subsequently apply repeated stimulation on the identified potentially stimulable locations to observe an exemplary neuronal pathway.

Quantification and automatized adaptive detection of in vivo and in vitro neuronal bursts based on signal complexity.

In this paper, we propose employing entropy values to quantify action potential bursts in electrophysiological measurements from the brain and neuronal cultures. Conventionally in the electrophysiological signal analysis, bursts are quantified by means of conventional measures such as their durations, and number of spikes in bursts. Here our main aim is to device metrics for burst quantification to provide for enhanced burst characterization. Entropy is a widely employed measure to quantify regularity/complexity of time series. Specifically, we investigate the applicability and differences of spectral entropy and sample entropy in the quantification of bursts in in vivo rat hippocampal measurements and in in vitro dissociated rat cortical cell culture measurement done with microelectrode arrays. For the task, an automatized and adaptive burst detection method is also utilized. Whereas the employed metrics are known from other applications, they are rarely employed in the assessment of burst in electrophysiological field potential measurements. Our results show that the proposed metrics are potential for the task at hand.

Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics.

In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESCs), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing statistics based on interspike interval (ISI) histograms. Moreover, the algorithm calculates ISI thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average (CMA) and skewness of the ISI histogram. Because of the adaptive nature of the proposed algorithm, its analysis power is not limited by the type of neuronal cell network at hand. We demonstrate the functionality of our algorithm with two different types of microelectrode array (MEA) data recorded from spontaneously active hESC-derived neuronal cell networks. The same data was also analyzed by two commonly employed burst detection algorithms and the differences in burst detection results are illustrated. The results demonstrate that our method is both adaptive to the firing statistics of the network and yields successful burst detection from the data. In conclusion, the proposed method is a potential tool for analyzing of hESC-derived neuronal cell networks and thus can be utilized in studies aiming to understand the development and functioning of human neuronal networks and as an analysis tool for in vitro drug screening and neurotoxicity assays.

Averaging in vitro cardiac field potential recordings obtained with microelectrode arrays.

Extracellular field potential (FP) recordings with microelectrode arrays (MEAs) from cardiomyocyte cultures offer a non-invasive way of studying the electrophysiological properties of these cells at the population level. Several studies have examined the FP properties of cardiomyocytes of various origins, including stem cell-derived cardiomyocytes. This focus reflects growing importance and interest in the field of MEA. High-quality cardiac FP signals are often difficult to obtain, especially from stem cell-derived cardiomyocyte cultures, which represent an important new field in cardiac electrophysiology. One way to improve the quality of these recordings is to average the cardiac FP signals. To date, however, no studies have examined the effect of averaging on cardiac FP signals. We report here that cardiac FP averaging can yield higher-quality signals than original individual FPs, and therefore promise more accurate detection of different phases and analysis of the cardiac FP signal. Averaged signals improved the signal-to-noise ratio (SNR), and obtaining reliable averages required approximately 50 cardiac cycles. We therefore propose that routine cardiac FP averaging can serve as a tool to compare the effects of different experimental conditions or stimuli on the properties of cardiac FPs.

Substantial variation in the cardiac differentiation of human embryonic stem cell lines derived and propagated under the same conditions--a comparison of multiple cell lines.

AIM: The differentiation efficiencies of human embryonic stem cell (hESC) lines differ from each other. To assess this in more detail we studied the cardiac differentiation of eight hESC lines derived in the same laboratory. RESULTS: Substantial variation in growth and in the ability to form beating areas was seen between the different hESC lines; line HS346 gave the best efficiency (9.4%), while HS293 did not differentiate into beating colonies at all. Nine germ layer and differentiation markers were quantified during early differentiation in four hESC lines. The expression levels of Brachyury T, MESP1 and NKX2.5 were highest in the most efficient cardiac line (HS346). A systematic characterization of the beating cells revealed proper cardiac marker expression, electrophysiological activity, and pharmacological response. CONCLUSIONS: The hESC lines derived in the same laboratory varied considerably in their potential to differentiate into beating cardiomyocytes. None of the expression markers could clearly predict cardiac differentiation potential, although the expression of early cardiomyogenic genes was upregulated in the best cardiac line. The proper cardiomyocyte characteristics and pharmacological response indicate that these cells could be used as a model for human cardiomyocytes in pharmacological and toxicological analyses when investigating new heart medications or cardiac side-effects.

Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro.

The production of functional human embryonic stem cell (hESC)-derived neuronal cells is critical for the application of hESCs in treating neurodegenerative disorders. To study the potential functionality of hESC-derived neurons, we cultured and monitored the development of hESC-derived neuronal networks on microelectrode arrays. Immunocytochemical studies revealed that these networks were positive for the neuronal marker proteins beta-tubulin(III) and microtubule-associated protein 2 (MAP-2). The hESC-derived neuronal networks were spontaneously active and exhibited a multitude of electrical impulse firing patterns. Synchronous bursts of electrical activity similar to those reported for hippocampal neurons and rodent embryonic stem cell-derived neuronal networks were recorded from the differentiated cultures until up to 4 months. The dependence of the observed neuronal network activity on sodium ion channels was examined using tetrodotoxin (TTX). Antagonists for the glutamate receptors NMDA [D(-)-2-amino-5-phosphonopentanoic acid] and AMPA/kainate [6-cyano-7-nitroquinoxaline-2,3-dione], and for GABAA receptors [(-)-bicuculline methiodide] modulated the spontaneous electrical activity, indicating that pharmacologically susceptible neuronal networks with functional synapses had been generated. The findings indicate that hESC-derived neuronal cells can generate spontaneously active networks with synchronous communication in vitro, and are therefore suitable for use in developmental and drug screening studies, as well as for regenerative medicine.

Independent component analysis of parameterized ECG signals.

Independent component analysis (ICA) of measured signals yields the independent sources, given certain fulfilled requirements. Properly parameterized signals provide a better view to the considered system aspects, while reducing the amount of data. It is little acknowledged that appropriately parameterized signals may be subjected to ICA, yielding independent components (ICs) displaying more clearly the investigated properties of the sources. In this paper, we propose ICA of parameterized signals, and demonstrate the concept with ICA of ST and R parameterizations of electrocardiogram (ECG) signals from ECG exercise test measurements from two coronary artery disease (CAD) patients.

Observing frequency content time evolution of independent hippocampal signals.

In this paper, we propose a method for observing frequency contents of independent hippocampal signals in time. The method is based on calculating independent component analysis (ICA) on electrophysiological multielectrode field potential measurements (MFPMs) in a running window. We have previously proposed a method for observing independently operating neural populations, i.e., functional populations (FUPOs) from MFPMs and outlined the concept, which is elaborated upon and extended in this paper, in order to facilitate analysis of functioning of the target brain area. In this paper, the proposed method is demonstrated with an example with three concurrent hippocampal measurements from an anesthetized rat brain. The proposed method can be applied in analysis of any recordings of neural networks in which contributions from a number of neural populations (NPs) are simultaneously recorded via a number of measurement points (MPs), as well in vivo as in vitro.