An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

Development of a Novel Platform for in vitro Electrophysiological Recording

The accurate monitoring of cell electrical activity is of fundamental importance for pharmaceutical research and pre-clinical trials that impose to check the cardiotoxicity of all new drugs. Traditional methods for preclinical evaluation of drug cardiotoxicity exploit animal models, which tend to be expensive, low throughput, and exhibit species-specific differences in cardiac physiology (Mercola, Colas and Willems, 2013). Alternative approaches use heterologous expression of cardiac ion channels in non-cardiac cells transfected with genetic material. However, the use of these constructs and the inhibition of specific ionic currents alone is not predictive of cardiotoxicity. Drug toxicity evaluation based on the human ether-\ue0-go-go-related gene (hERG) channel, for example, leads to a high rate of false-positive cardiotoxic compounds, increasing drug attrition at the preclinical stage. Consequently, from 2013, the Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative focused on experimental methods that identify cardiotoxic drugs and to improve upon prior models that have largely used alterations in the hERG potassium ion channel. The most predictive models for drug cardiotoxicity must recapitulate the complex spatial distribution of the physiologically distinct myocytes of the intact adult human heart. However, intact human heart preparations are inherently too costly, difficult to maintain, and, hence, too low throughput to be implemented early in the drug development pipeline. For these reasons the optimization of methodologies to differentiate human induced Pluripotent Stem Cells (hiPSCs) into cardiomyocytes (CMs) enabled human CMs to be mass-produced in vitro for cardiovascular disease modeling and drug screening (Sharma, Wu and Wu, 2013). These hiPSC-CMs functionally express most of the ion channels and sarcomeric proteins found in adult human CMs and can spontaneously contract. Recent results from the CiPA initiative have confirmed that, if utilized appropriately, the hiPSC-CM platform can serve as a reliable alternative to existing hERG assays for evaluating arrhythmogenic compounds and can sensitively detect the action potential repolarization effects associated with ion channel\u2013blocking drugs (Millard et al., 2018). Data on drug-induced toxicity in hiPSC-CMs have already been successfully collected by using several functional readouts, such as field potential traces using multi-electrode array (MEA) technology (Clements, 2016), action potentials via voltage-sensitive dyes (VSD) (Blinova et al., 2017) and cellular impedance (Scott et al., 2014). Despite still under discussion, scientists reached a consensus on the value of using electrophysiological data from hiPSC-CM for predicting cardiotoxicity and how it\u2019s possible to further optimize hiPSC-CM-based in vitro assays for acute and chronic cardiotoxicity assessment. In line with CiPA, therefore, the use of hiPSC coupled with MEA technology has been selected as promising readout for these kind of experiments. These platforms are used as an experimental model for studying the cardiac Action Potentials (APs) dynamics and for understanding some fundamental principles about the APs propagation and synchronization in healthy heart tissue. MEA technology utilizes recordings from an array of electrodes embedded in the culture surface of a well. When cardiomyocytes are grown on these surfaces, spontaneous action potentials from a cluster of cardiomyocytes, the so called functional syncytium, can be detected as fluctuations in the extracellular field potential (FP). MEA measures the change in FP as the action potential propagates through the cell monolayer relative to the recording electrode, neverthless FP in the MEA do not allows to recapitualte properly the action potential features. It is clear, therefore, that a MEA technology itself is not enough to implement cardiotoxicity assays on hIPSCs-CMs. Under this issue, researchers spread in the world started to think about solutions to achieve a platform able to works both at the same time as a standard MEA and as a patch clamp, allowing the recording of extracellular signals as usual, with the opportunity to switch to intracellular-like signals from the cytosol. This strong interest stimulated the development of methods for intracellular recording of action potentials. Currently, the most promising results are represented by multi-electrode arrays (MEA) decorated with 3D nanostructures that were introduced in pioneering papers (Robinson et al., 2012; Xie et al., 2012), culminating with the recent work from the group of H. Park (Abbott et al., 2017) and of F. De Angelis (Dipalo et al., 2017). In these articles, they show intracellular recordings on electrodes refined with 3D nanopillars after electroporation and laser optoporation from different kind of cells. However, the requirement of 3D nanostructures set strong limitations to the practical spreading of these techniques. Thus, despite pioneering results have been obtained exploiting laser optoporation, these technologies neither been applied to practical cases nor reached the commercial phase. This PhD thesis introduces the concept of meta-electrodes coupled with laser optoporation for high quality intracellular signals from hiPSCs-CM. These signals can be recorded on high-density commercial CMOS-MEAs from 3Brain characterized by thousands of electrode covered by a thin film of porous Platinum without any rework of the devices, 3D nanostructures or circuitry for electroporation7. Subsequently, I attempted to translate these unique features of low invasiveness and reliability to other commercial MEA platforms, in order to develop a new tool for cardiac electrophysiological accurate recordings. The whole thesis is organized in three main sections: a first single chapters that will go deeper in the scientific and technological background, including an explanation of the cell biology of hiPSCs-CM followed by a full overview of MEA technology and devices. Then, I will move on state-of-the-art approaches of intracellular recording, discussing many works from the scientific literature. A second chapter will describe the main objectives of the whole work, and a last chapter with the main results of the activity. A final chapter will resume and recapitulate the conclusion of the work

A modular multi electrode array system for electrogenic cell characterisation and cardiotoxicity applications

Multi electrode array (MEA) systems have evolved from custom-made experimental tools, exploited for neural research, into commercially available systems that are used throughout non-invasive electrophysiological study. MEA systems are used in conjunction with cells and tissues from a number of differing organisms (e.g. mice, monkeys, chickens, plants). The development of MEA systems has been incremental over the past 30 years due to constantly changing specific bioscientific requirements in research. As the application of MEA systems continues to diversify contemporary commercial systems are requiring increased levels of sophistication and greater throughput capabilities. [Continues.

Development of in-vitro µ-channel devices for continous long-term monitoring of neuron circuit development

In this thesis various methods are presented towards long-term electrophysiological monitoring of in-vitro neuron cultures in µ-channel devices. A new µ-channel device has been developed. The StarPoM device offers multiple culture chambers connected with µ-channels allowing to study communication between neuron populations. For its fabrication an advanced multi level SU-8 soft-lithography master was developed that can mold µ-channels and culture wells simultaneously. The problem of aligning features across a thick SU-8 layer has been solved by integrating a chrome mask into the substrate and then using backside exposure through the chrome mask. A long-term monitoring of neuron electrophysiological activity has been conducted continuously during 14 days in the StarPoM device. For the analysis of the recorded dataset a new software tool-chain has been created with the goal of high processing performance. The two most advanced components - O1Plot and ISI viewer - offer high performance visualization of time series data with event or interval annotation and visualization of inter-spike interval histograms for fast discovery of correlations between spike units on a device. The analysis of the 14 day recording revealed that signals can be recorded from day 4/5 onwards. While maximum spike amplitudes in kept rising during the 14 days and reached up to 3.16 mV, the average spike amplitudes reached their maximum of 0.1-0.3 mV within 6 to 8 days and then kept the amplitudes stable. To better understand the biophysics of signal generation in µ-channels, the influence of µ-channel length on signal amplitude was studied. A model based on the passive cable theory was developed showing that spike amplitude rises with channel length for µ-channels < 250 µm. In longer µ-channels, further growth of spike amplitude is inhibited by cancellation of positive and negative spike phase. Also, clogging of the µ-channel entrances by cells and debris helps to enhance signal amplification

Materials and neuroscience: validating tools for large-scale, high-density neural recording

Extracellular recording remains the only technique capable of measuring the activity of many neurons simultaneously with a sub-millisecond precision, in multiple brain areas, including deep structures. Nevertheless, many questions about the nature of the detected signal and the limitations/capabilities of this technique remain unanswered. The general goal of this work is to apply the methodology and concepts of materials science to answer some of the major questions surrounding extracellular recording, and thus take full advantage of this seminal technique. We start out by quantifying the effect of electrode impedance on the amplitude of measured extracellular spikes and background noise. Can we improve data quality by lowering electrode impedance? We demonstrate that if the proper recording system is used, then the impedance of a microelectrode, within the range typical of standard polytrodes (~ 0.1 to 2 MΩ), does not significantly affect a neural spike amplitude or the background noise, and therefore spike sorting. In addition to improving the performance of each electrode, increasing the number of electrodes in a single neural probe has also proven advantageous for simultaneously monitoring the activity of more neurons with better spatiotemporal resolution. How can we achieve large-scale, highdensity extracellular recordings without compromising brain tissue? Here we report the design and in vivo validation of a complementary metal–oxide–semiconductor (CMOS)-based scanning probe with 1356 electrodes arranged along approximately 8 mm of a thin shaft (50 μm thick and 100 μm wide). Additionally, given the ever-shrinking dimensions of CMOS technology, there is a drive to fabricate sub-cellular electrodes (< 10 μm). Therefore, to evaluate electrode configurations for future probe designs, several recordings from many different brain regions were performed with an ultra-dense probe containing 255 electrodes, each with a geometric area of 5 x 5 μm and a pitch of 6 μm. How can we validate neural probes with different electrode materials/configurations and different sorting algorithms? We describe a new procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. We gathered a dataset of paired-recordings, which is available online. The “ground truth” data, for which one knows exactly when a neuron in the vicinity of an extracellular probe generates an action potential, has been used for several groups to validate and quantify the performance of new algorithms to automatically detect/sort single-units

Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces