Microelectrode Array Data Research Articles

Development of an evaluation method for addictive compounds based on electrical activity of human iPS cell-derived dopaminergic neurons using microelectrode array.

Addiction is known to occur through the consumption of substances such as pharmaceuticals, illicit drugs, food, alcohol and tobacco. These addictions can be viewed as drug addiction, resulting from the ingestion of chemical substances contained in them. Multiple neural networks, including the reward system, anti-reward/stress system and central immune system in the brain, are believed to be involved in the onset of drug addiction. Although various compound evaluations using microelectrode array (MEA) as an in vitro testing methods to evaluate neural activities have been conducted, methods for assessing addiction have not been established. In this study, we aimed to develop an in vitro method for assessing the addiction of compounds, as an alternative to animal experiments, using human iPS cell-derived dopaminergic neurons with MEA measurements. MEA data before and after chronic exposure revealed specific changes in addictive compounds compared to non-addictive compounds, demonstrating the ability to estimate addiction of compound. Additionally, conducting gene expression analysis on cultured samples after the tests revealed changes in the expression levels of various receptors (nicotine, dopamine and GABA) due to chronic administration of addictive compounds, suggesting the potential interpretation of these expression changes as addiction-like responses in MEA measurements. The addiction assessment method using MEA measurements in human iPS cell-derived dopaminergic neurons conducted in this study proves effective in evaluating addiction of compounds on human neural networks.

In Vitro Pharmacological Modulation of PIEZO1 Channels in Frontal Cortex Neuronal Networks.

PIEZO1 is a mechanosensitive ion channel expressed in various organs, including but not limited to the brain, heart, lungs, kidneys, bone, and skin. PIEZO1 has been implicated in astrocyte, microglia, capillary, and oligodendrocyte signaling in the mammalian cortex. Using murine embryonic frontal cortex tissue, we examined the protein expression and functionality of PIEZO1 channels in cultured networks leveraging substrate-integrated microelectrode arrays (MEAs) with additional quantitative results from calcium imaging and whole-cell patch-clamp electrophysiology. MEA data show that the PIEZO1 agonist Yoda1 transiently enhances the mean firing rate (MFR) of single units, while the PIEZO1 antagonist GsMTx4 inhibits both spontaneous activity and Yoda1-induced increase in MFR in cortical networks. Furthermore, calcium imaging experiments revealed that Yoda1 significantly increased the frequency of calcium transients in cortical cells. Additionally, in voltage clamp experiments, Yoda1 exposure shifted the cellular reversal potential towards depolarized potentials consistent with the behavior of PIEZO1 as a non-specific cation-permeable channel. Our work demonstrates that murine frontal cortical neurons express functional PIEZO1 channels and quantifies the electrophysiological effects of channel activation in vitro. By quantifying the electrophysiological effects of PIEZO1 activation in vitro, our study establishes a foundation for future investigations into the role of PIEZO1 in neurological processes and potential therapeutic applications targeting mechanosensitive channels in various physiological contexts.

Inference of network connectivity from temporally binned spike trains

BackgroundProcessing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. New methodWe infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. ResultsResults reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. Comparison with existing method(s)Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. ConclusionsWe provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.

Comparative microelectrode array data of the functional development of hPSC-derived and rat neuronal networks

We present a dataset of microelectrode array (MEA) recordings from human pluripotent stem cell (hPSC)-derived and rat embryonic cortical neurons during their in vitro maturation. The data were prepared to assess extracellularly recorded spontaneous activity and to compare the functional development of these neuronal networks. In addition to recordings of spontaneous activity, we provide pharmacological responses of hPSC-derived and rat cortical cultures at their mature stage. Together with the recorded electrode raw data, we share the analysis code to form a comprehensive dataset including spike times, spike waveforms, burst activity and network synchronization metrics calculated with two different connectivity estimators. Moreover, we provide the analysis code that produced the key scientific findings published previously with this dataset. This large dataset enables investigation of the functional aspects of maturing cortical neuronal networks and provides substantial parameters to assess the differences and similarities between hPSC-derived and rat cortical networks in vitro. This publicly available dataset will be beneficial, especially for experimental and computational neuroscientists.

Culture of Rat Primary Cortical Cells for Microelectrode Array (MEA) Recordings to Screen for Acute and Developmental Neurotoxicity.

Neurotoxicity testing of chemicals, drug candidates, and environmental pollutants still relies on extensive in vivo studies that are very costly, time-consuming, and ethically debated due to the large number of animals typically used. Currently, rat primary cortical cultures are widely used for in vitro neurotoxicity studies, as they closely resemble the in vitro brain with respect to the diversity of cell types, their physiological functions, and the pathological processes that they undergo. Common in vitro assays for neurotoxicity screening often focus on very target-specific endpoints such as morphological, biochemical, or electrophysiological changes, and such narrow focus can hamper translation and interpretation. Microelectrode array (MEA) recordings provide a non-invasive platform for extracellular recording of electrical activity of cultured neuronal cells, thereby enabling the evaluation of changes in neuronal (network) function as a sensitive and integrated endpoint for neurotoxicity screening. Here, we describe an in vitro approach for assessing changes in neuronal network function as a measure for neurotoxicity, using rat primary cortical cultures grown on MEAs. We provide a detailed protocol for the culture of rat primary cortical cells, and describe several experimental procedures to address acute, subchronic, and chronic exposure scenarios. We additionally describe the steps for processing and analyzing MEA and cell viability data. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Isolation and culture of rat primary cortical cells on 48-well MEA plates Support Protocol 1: Pretreatment and washing of 48-well MEA plates before first use or for re-use Support Protocol 2: Coating of 48-well MEA plates with 0.1% PEI solution Basic Protocol 2: MEA measurements during acute exposure Alternate Protocol 1: MEA measurements during subchronic exposure Alternate Protocol 2: MEA measurements during chronic exposure Support Protocol 3: Determination of cell viability after MEA experiments Basic Protocol 3: MEA data processing Basic Protocol 4: Analyzing MEA experiments after acute and subchronic exposure Alternate Protocol 3: Analyzing MEA experiments after chronic exposure.

Development of an objective index, neural activity score (NAS), reveals neural network ontogeny and treatment effects on microelectrode arrays

Microelectrode arrays (MEAs) are valuable tools for electrophysiological analysis, providing assessment of neural network health and development. Analysis can be complex, however, requiring intensive processing of large data sets consisting of many activity parameters, leading to information loss as studies subjectively report relatively few metrics in the interest of simplicity. In screening assays, many groups report simple overall activity (i.e. firing rate) but omit network connectivity changes (e.g. burst characteristics and synchrony) that may not be evident from basic parameters. Our goal was to develop an objective process to capture most of the valuable information gained from MEAs in neural development and toxicity studies. We implemented principal component analysis (PCA) to reduce the high dimensionality of MEA data. Upon analysis, we found the first principal component was strongly correlated to time, representing neural culture development; therefore, factor loadings were used to create a single index score—named neural activity score (NAS)—reflecting neural maturation. For validation, we applied NAS to studies analyzing various treatments. In all cases, NAS accurately recapitulated expected results, suggesting viability of NAS to measure network health and development. This approach may be adopted by other researchers using MEAs to analyze complicated treatment effects and multicellular interactions.

Cytosolic PSD-95 interactor alters functional organization of neural circuits and AMPA receptor signaling independent of PSD-95 binding.

Cytosolic PSD-95 interactor (cypin) regulates many aspects of neuronal development and function, ranging from dendritogenesis to synaptic protein localization. While it is known that removal of postsynaptic density protein-95 (PSD-95) from the postsynaptic density decreases synaptic N-methyl-D-aspartate (NMDA) receptors and that cypin overexpression protects neurons from NMDA-induced toxicity, little is known about cypin’s role in AMPA receptor clustering and function. Experimental work shows that cypin overexpression decreases PSD-95 levels in synaptosomes and the PSD, decreases PSD-95 clusters/μm2, and increases mEPSC frequency. Analysis of microelectrode array (MEA) data demonstrates that cypin or cypinΔPDZ overexpression increases sensitivity to CNQX (cyanquixaline) and AMPA receptor-mediated decreases in spike waveform properties. Network-level analysis of MEA data reveals that cypinΔPDZ overexpression causes networks to be resilient to CNQX-induced changes in local efficiency. Incorporating these findings into a computational model of a neural circuit demonstrates a role for AMPA receptors in cypin-promoted changes to networks and shows that cypin increases firing rate while changing network functional organization, suggesting cypin overexpression facilitates information relay but modifies how information is encoded among brain regions. Our data show that cypin promotes changes to AMPA receptor signaling independent of PSD-95 binding, shaping neural circuits and output to regions beyond the hippocampus.

Classification of pseudocalcium visual responses from mouse retinal ganglion cells.

Recently, a detailed catalog of 32 retinal ganglion cell (RGC) visual response patterns in mouse has emerged. However, the 10,000 samples required for this catalog-based on fluorescent signals from a calcium indicator dye-are much harder to acquire from the extracellular spike train recordings underlying our bionic vision research. Therefore, we sought to convert spike trains into pseudocalcium signals so that our data could be directly matched to the 32 predefined, calcium signal-based groups. A microelectrode array (MEA) was used to record spike trains from mouse RGCs of 29 retinas. Visual stimuli were adapted from the Baden et al. study; including moving bars, full-field contrast and temporal frequency chirps, and black-white and UV-green color flashes. Spike train histograms were converted into pseudocalcium traces with an OGB-1 convolution kernel. Response features were extracted using sparse principal components analysis to match each RGC to one of the 32 RGC groups. These responses mapped onto of the 32 previously described groups; however, some of the groups remained unmatched. Thus, adaptation of the Baden et al. methodology for MEA recordings of spike trains instead of calcium recordings was partially successful. Different classification methods, however, will be needed to define clear RGC groups from MEA data for our bionic vision research. Nevertheless, others may pursue a pseudocalcium approach to reconcile spike trains with calcium signals. This work will help to guide them on the limitations and potential pitfalls of such an approach.

Enhancing Stem Cell‐based Toxicity Assays by Engineering the Culture Niche in a High‐throughput, Assay‐agnostic Manner

Human induced pluripotent stem cell (hiPSC)‐based assays hold great promise for ameliorating human disease and improving the translation of new therapies, but they have shortcomings that hamper their ability to accurately model human physiology. For example, hiPSC‐Cardiomyocytes (CMs) mis‐classify several compounds of known cardiotoxicity. One likely source of this is the maturity of the cells; most culture techniques result in hiPSC‐CMs that express fetal phenotypes, leading to inaccurate or difficult‐to‐translate results. While engineering the extracellular matrix (ECM) has demonstrated improvements to both structural and functional phenotypes, it is often hampered by challenges with integration into industry‐standard assays or instrumentation. Further, the ability to engineer increasingly intricate and multi‐faceted artificial culture environments often drives decisions that trade off biological complexity with experimental throughput. Here, we developed bioengineering techniques to create biomimetic patterns that mimic the shape and structure of the ECM on a transparent polymer‐coated glass layer. The resultant structure directs focal adhesion assembly mechanisms in individual cells to drive higher‐ordered structuring of 2D tissues. This forms 2D anisotropic syncytia from iPSC‐CMs in industry‐standard microplate formats that are compatible with transmitted light microscopy. The method is extended to micro‐electrode arrays (MEA) in a manner that maintains both the quality of MEA recordings and baseline electrophysiological properties. iPSC‐CMs grown this way exhibit several structural and biochemical phenotypes including adult protein isoform expression, myofibril alignment, and sarcomere spacing, length, and width. We show that biomimetic cues enhance the electrophysiological response of cardiomyocytes to various drugs of known effect. Since ECM structuring recapitulates the polarized nature of cells and their concomitant gap junctions, we used the gap junction blocker carbenoxolone for validation studies. Polarized tissues enable differential conduction block measurements between longitudinal and transverse directions. Our MEA data show that patterned tissues exhibit increased sensitivity to carbenoxolone over unpatterned constructs (IC50’s of 14.71 nM versus 398.1 nM, respectively) comparing favorably to clinically relevant unbound concentrations reported in the low nM range. Our biomimetic engineering approach is instrument‐ and assay agnostic, effective on a variety of cell types and lines (including neuronal cells; examples to be presented), and compatible with high‐throughput approaches.Support or Funding InformationNational Institutes of Health Small Business Innovation Research Grant.(A) decellularized ex‐vivo cardiac tissue slices demonstrate a polarized meshwork of extracellular matrix (ECM) fibers; (B) our biomimetic patterning technique recapitulates the nanoscale structure and orientation of the ECM; (C) the technique can be expanded to the surface of high‐throughput MEAs without degradation to electrical signal detection while maintaining the nanoscale pattern (inset); (D) FCDI iCell2 Cardiomyocytes cultured on traditional plastic surfaces show unstructured morphology; (E) iCell2 Cardiomyocytes cultured on our biomimetic surfaces recapitulate the anisotropic structure of adult heart tissue.Figure 1(A) sarcomere lengths (B) and widths of cells on biomimetic nanosurfaces (green) compared to conventional surfaces (gray) are increased; (C) adult isoforms of structural proteins are enhanced with biomimetic nanopatterning; (D) patterned cells exhibit higher oxygen consumption rate; (E) patch clamp data demonstrate increased action potential duration on patterned tissues; (F) asymmetric conduction on patterned tissues enhances the sensitivity of MEA assays to conduction blockers;Figure 2

Trial to detect significant metrics for drug-induced seizure liability using microelectrode arrays data analysis and primary rodent neurons: From multi-site pilot study of the HESI NeuTox consortium in collaboration with CSAHi and iNCENS

Trial to detect significant metrics for drug-induced seizure liability using microelectrode arrays data analysis and primary rodent neurons: From multi-site pilot study of the HESI NeuTox consortium in collaboration with CSAHi and iNCENS

Evidence for the impact of BAG3 on electrophysiological activity of primary culture of neonatal cardiomyocytes.

Homeostasis of proteins involved in contractility of individual cardiomyocytes and those coupling adjacent cells is of critical importance as any abnormalities in cardiac electrical conduction may result in cardiac irregular activity and heart failure. Bcl2-associated athanogene 3 (BAG3) is a stress-induced protein whose role in stabilizing myofibril proteins as well as protein quality control pathways, especially in the cardiac tissue, has captured much attention. Mutations of BAG3 have been implicated in the pathogenesis of cardiac complications such as dilated cardiomyopathy. In this study, we have used an in vitro model of neonatal rat ventricular cardiomyocytes to investigate potential impacts of BAG3 on electrophysiological activity by employing the microelectrode array (MEA) technology. Our MEA data showed that BAG3 plays an important role in the cardiac signal generation as reduced levels of BAG3 led to lower signal frequency and amplitude. Our analysis also revealed that BAG3 is essential to the signal propagation throughout the myocardium, as the MEA data-based conduction velocity, connectivity degree, activation time, and synchrony were adversely affected by BAG3 knockdown. Moreover, BAG3 deficiency was demonstrated to be connected with the emergence of independently beating clusters of cardiomyocytes. On the other hand, BAG3 overexpression improved the activity of cardiomyocytes in terms of electrical signal amplitude and connectivity degree. Overall, by providing more in-depth analyses and characterization of electrophysiological parameters, this study reveals that BAG3 is of critical importance for electrical activity of neonatal cardiomyocytes.

MeaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.

Here we present an open-source R package ‘meaRtools’ that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.

Ginkgo biloba L. (Ginkgoaceae) Leaf Extract Medications From Different Providers Exhibit Differential Functional Effects on Mouse Frontal Cortex Neuronal Networks.

Background: Details of the extraction and purification procedure can have a profound impact on the composition of plant-derived extracts, and thus on their efficacy and safety. So far, studies with head-to-head comparison of the pharmacology of Ginkgo extracts rendered by different procedures have been rare.Objective: The objective of this study was to explore whether Ginkgo biloba L. (Ginkgoaceae) leaf extract medications of various sources protect against amyloid beta toxicity on primary mouse cortex neurons growing on microelectrode arrays, and whether the effects differ between different Ginkgo extracts.Design: Our brain-on-chip platform integrates microelectrode array data recorded on neuronal tissue cultures from embryonic mouse cortex. Amyloid beta 42 (Aβ42) and various Ginkgo extract preparations were added to the networks in vitro before evaluation of electrophysiological parameters by multi-parametric analysis. A Multi-variate data analysis, called Effect Score, was designed to compare effects between different products.Results: The results show that Ginkgo extracts protected against Aβ42-induced electrophysiological alterations. Different Ginkgo extracts exhibited different effects. Of note, the reference Ginkgo biloba L. (Ginkgoaceae) leaf medication Tebonin had the most pronounced rescuing effect.Conclusion: Here, we show for the first time a side-by-side analysis of a large number of Ginkgo medications in a relevant in vitro system modeling early functional effects induced by amyloid beta peptides on neuronal transmission and connectivity. Ginkgo biloba L. (Ginkgoaceae) leaf extract from different manufactures exhibit differential functional effects in this neural network model. This in-depth analysis of functional phenotypes of neurons cultured on MEAs chips allows identifying optimal plant extract formulations protecting against toxin-induced functional effects in vitro.

Method for MEA Data Analysis of Drug-treated Rat Primary Neurons and Human iPSC-derived Neurons to Evaluate the Risk of Drug-induced Seizures

Use of the microelectrode array (MEA) system to record spontaneous neuron activity from networks of cultured neurons has potential as a good risk evaluation method for drug-induced seizure events. Spontaneous electrical activity in neural networks consists of action potential spikes and organized patterns of action potential bursts. In both potentiated rodent primary neurons and human induced pluripotent stem cell (iPSC)-derived neurons, an epileptogenic response pattern manifests as a synchronized burst from spatially separated neurons. Here, we delineate how to perform MEA experiments using cultured neurons, and how to analyze the MEA data to detect drug-induced seizurogenic abnormalities. Usually, a drug's effects, as shown by MEA data, include changes in spike frequency, inter-spike intervals (ISI), burst frequency, burst duration, spikes in a burst, etc. Subsequently, seizurogenic events are evidenced by changes in synchronized burst phenotypes from spatially separated multiple channels in an MEA probe, such as a change in the cross correlation of the spike events from all channels in an MEA probe, or a change in histogram from the sum of ISI for all channels in a probe, etc. We attempted to depict an epileptogenic marker using a histogram of the sum of spikes for all channels in an MEA probe. Verification of these metrics for drug induced abnormalities is ongoing in various collaboration organizations, including the Consortium for Safety Assessment using Human iPS Cells (CSAHi), iPS Non-clinical Experiments for the Nervous System (iNCENS). Collaboration networks for the utilization of iPSC-derived cells during drug development are also summarized here.

Functional epilepsy modelling – a novel platform for studying human stem cell -derived neuronal networks

Event Abstract Back to Event Functional epilepsy modelling – a novel platform for studying human stem cell -derived neuronal networks Anssi Pelkonen1*, Lassi Sukki2, Tomi Ryynänen3, Tanja Hyvärinen1, Jukka Lekkala3, Pasi Kallio2 and Susanna Narkilahti1 1 University of Tampere, BioMediTech and Faculty of Medicine and Life Sciences, Finland 2 Tampere University of Technology, Faculty of Biomedical Sciences and Engineering, Finland 3 Tampere University of Technology, Faculty of Biomedical Sciences and Engineering, Finland Motivation Epilepsy is characterized by spread of abnormal electrical activity within and between neuronal networks. New epilepsy treatments are tested in rodent models, but less than 10 % of putative drugs are accepted for use[1] and 30 % of epilepsy patients still do not respond to existing treatments[2]. Therefore, new models are needed for epilepsy modelling and drug testing. Human pluripotent stem cell (hPSC) –derived neural networks are the most promising base material for such models, but they alone cannot model the connections, feedback loops and spread of abnormal activity between neuronal networks. Material and Methods Our solution is an innovative new platform which combines human stem cell –derived neural networks, microfluidic devices and microelectrode array (MEA) technology. hPSC–derived cortical neurons are cultured in the platform where they form functional networks. The networks are cultured in a microfluidic cell culture device which isolates the stem cell derived networks and the axonal connections between them. The device is comprised of polydimethylsiloxane (PDMS), and shaped using a micro- and millimeter-scale mold which consists of SU-8 and stainless steel (3D printed using selective laser melting (SLM)). The device is connected to a custom made MEA chip for measuring the electrical activity within and between the networks. The MEA chip is compatible with a commercially available MEA system (MEA2100, Multi Channel Systems, Reutlingen, Germany). Results The produced neuronal networks are highly active and respond to glutamatergic and GABAergic factors. The PDMS device allows the formation of axonal connections between separated neural networks. The PDMS device and the MEA chip are a suitable environment for the neural networks, which remained viable and active up to 12 weeks. The MEA data shows that the network activity in the device develops into synchronous, network-to-network connected bursts in 3-5 weeks. Individual networks can be subjected to seizure-inducing drugs, and the effects of the drug treatments are clearly visible in the MEA-data. Conclusion The platform is suitable for studying network activity and connectivity of human stem cell –derived neural networks. Therefore, it has the potential to be used in disease modelling and drug testing for epilepsy and other central nervous system disorders. Acknowledgements Funding: Business Finland (formerly known as Finnish Funding Agency for Technology and Innovation (TEKES)); Academy of Finland (Decision No. 311017); Finnish Cultural Foundation (personal grant, TR); Juliana von Wendt fund (personal grant, AP). The work was supported by the Imaging Facility, Facility of Electrophysiological Measurements and iPS Cells Facility (Uni. Tampere). Thanks to Arla Tanner for technical assistance and Juha Heikkilä for assistance with measurement and analysis software.

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.

Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy.

Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive burst detection. The main emphasis is, if network-wide adaptive burst detection can provide new insights into the assessment of network activity. Therefore, we propose a modification to the previously introduced inter-spike interval (ISI) histogram based cumulative moving average (CMA) algorithm to analyze multiple spike trains simultaneously. The network size can be freely defined, e.g., to include all the electrodes in a microelectrode array (MEA) recording. Additionally, the method can be applied on a series of measurements on the same network to pool the data for statistical analysis. Firstly, we apply both the original CMA-algorithm and our proposed network-wide CMA-algorithm on artificial spike trains to investigate how the modification changes the burst detection. Thereafter, we use the algorithms on MEA data of spontaneously active chemically manipulated in vitro rat cortical networks. Moreover, we compare the synchrony of the detected bursts introducing a new burst synchrony measure. Finally, we demonstrate how the bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results show that the proposed network wide adaptive burst detection provides a method to unify the burst definition in the whole network and thus improves the assessment and classification of the neuronal activity, e.g., the effects of different pharmaceuticals. The results indicate that the novel method is adaptive enough to be usable on networks with different dynamics, and it is especially feasible when comparing the behavior of differently spiking networks, for example in developing networks.

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.

Development of MATLAB software to control data acquisition from a multichannel systems multi-electrode array.

A Multichannel Systems (MCS) microelectrode array data acquisition (DAQ) unit is used to collect multichannel electrograms (EGM) from a Langendorff perfused rabbit heart system to study sudden cardiac death (SCD). MCS provides software through which data being processed by the DAQ unit can be displayed and saved, but this software's combined utility with MATLAB is not very effective. MCSs software stores recorded EGM data in a MathCad (MCD) format, which is then converted to a text file format. These text files are very large, and it is therefore very time consuming to import the EGM data into MATLAB for real-time analysis. Therefore, customized MATLAB software was developed to control the acquisition of data from the MCS DAQ unit, and provide specific laboratory accommodations for this study of SCD. The developed DAQ unit control software will be able to accurately: provide real time display of EGM signals; record and save EGM signals in MATLAB in a desired format; and produce real time analysis of the EGM signals; all through an intuitive GUI.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.