Sergio Martinoia

Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development.

In vitro cultured neuronal networks coupled to microelectrode arrays (MEAs) constitute a valuable experimental model for studying changes in the neuronal dynamics at different stages of development. After a few days in culture, neurons start to connect each other with functionally active synapses, forming a random network and displaying spontaneous electrophysiological activity. The patterns of collective rhythmic activity change in time spontaneously during in vitro development. Such activity-dependent modifications play a key role in the maturation of the network and reflect changes in the synaptic efficacy, fact widely recognized as a cellular basis of learning, memory and developmental plasticity. Getting advantage from the possibilities offered by the MEAs, the aim of our study is to analyze and characterize the natural changes in dynamics of the electrophysiological activity at different ages of the culture, identifying peculiar steps of the spontaneous evolution of the network. The main finding is that between the second and the third week of culture, the network completely changes its electrophysiological patterns, both in terms of spiking and bursting activity and in terms of cross-correlation between pairs of active channels. Then the maturation process can be characterized by two main phases: modulation and shaping in the synaptic functional connectivity of the network (within the first and second week) and general moderate correlated activity, spread over the entire network, with connections properly formed and stabilized (within the fourth and fifth week).

SELF-ORGANIZATION AND NEURONAL AVALANCHES IN NETWORKS OF DISSOCIATED CORTICAL NEURONS

Dissociated cortical neurons from rat embryos cultured onto micro-electrode arrays exhibit characteristic patterns of electrophysiological activity, ranging from isolated spikes in the first days of development to highly synchronized bursts after 3-4 weeks in vitro. In this work we analyzed these features by considering the approach proposed by the self-organized criticality theory: we found that networks of dissociated cortical neurons also generate spontaneous events of spreading activity, previously observed in cortical slices, in the form of neuronal avalanches. Choosing an appropriate time scale of observation to detect such neuronal avalanches, we studied the dynamics by considering the spontaneous activity during acute recordings in mature cultures and following the development of the network. We observed different behaviors, i.e. sub-critical, critical or super-critical distributions of avalanche sizes and durations, depending on both the age and the development of cultures. In order to clarify this variability, neuronal avalanches were correlated with other statistical parameters describing the global activity of the network. Criticality was found in correspondence to medium synchronization among bursts and high ratio between bursting and spiking activity. Then, the action of specific drugs affecting global bursting dynamics (i.e. acetylcholine and bicuculline) was investigated to confirm the correlation between criticality and regulated balance between synchronization and variability in the bursting activity. Finally, a computational model of neuronal network was developed in order to interpret the experimental results and understand which parameters (e.g. connectivity, excitability) influence the distribution of avalanches. In summary, cortical neurons preserve their capability to self-organize in an effective network even when dissociated and cultured in vitro. The distribution of avalanche features seems to be critical in those cultures displaying medium synchronization among bursts and poor random spiking activity, as confirmed by chemical manipulation experiments and modeling studies.

Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks

This paper presents a chip-based electrophysiological platform enabling the study of micro- and macro-circuitry in in-vitro neuronal preparations. The approach is based on a 64x64 microelectrode array device providing extracellular electrophysiological activity recordings with high spatial (21 microm of electrode separation) and temporal resolution (from 0.13 ms for 4096 microelectrodes down to 8 micros for 64 microelectrodes). Applied to in-vitro neuronal preparations, we show how this approach enables neuronal signals to be acquired for investigating neuronal activity from single cells and microcircuits to large scale neuronal networks. The main elements of the platform are the metallic microelectrode array (MEA) implemented in Complementary Metal Oxide Semiconductor (CMOS) technology similar to a light imager, the in-pixel integrated low-noise amplifiers (11 microVrms) and the high-speed random addressing logic. The chip is combined with a real-time acquisition system providing the capability to record at 7.8 kHz/electrode the whole array and to process the acquired signals.

A behavioral macromodel of the ISFET in SPICE

Abstract Physico-chemical models of the ISFET (Ion-Sensitive Field-Effect Transistor) were developed by the authors in the past, as SPICE built-in models (BIOSPICE). This approach has some drawbacks, i.e., the need of availability of the program source, a deep knowledge of the code subroutines and structure, and the need of compiling the whole program when a new model has to be implemented or when modifications to the models have to be made. To overcome these drawbacks, a more general and user-friendly approach is presented. It consists of a behavioral macromodel that can be used in conjunction with the most commercial SPICE versions. The behavior of the proposed macromodel has been validated by comparing the results with those obtained by BIOSPICE physico-chemical models and experimental measurements. The proposed macromodel is shown to operate also under subthreshold conditions that can be considered as a promising operating mode for large multisensor ISFET-based integrated systems.

Development of ISFET array-based microsystems for bioelectrochemical measurements of cell populations

Monitoring the bioelectrochemical activity of living cells with sensor array-based microsystems represents an emerging technique in a large area of biomedical applications, ranging from basic research to various fields of pharmacological analyses. The main appeal is the ability of these miniaturised microsystems to perform, in real time, non-invasive in-vitro investigations of the physiological state of a cell population. In this paper, we present two different microsystems designed for multisite monitoring of the physiological state of a cell population. The first microsystem, intended for cellular metabolism monitoring, consists of an array of 12 spatially distributed ISFETs to detect small pH variations induced by the cell population. The second microsystem consists of an array of 40 ISFETs and 20 gold microelectrodes and it has been designed to monitor the electrical activity of neurons. This is achieved by direct coupling of the neuronal culture with the ISFET sensitive layer and by utilising gold microelectrodes for neuronal electrical stimulation.

Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks.

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.

A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals.

The spike represents the fundamental bit of information transmitted by the neurons within a network in order to communicate. Then, given the importance of the spike rate as well as the spike time for coding the activity generated at the level of a cell assembly, a relevant issue in extracellular electrophysiology is the correct identification of the spike in multisite recordings from brain areas or neuronal networks. In this paper, we present a novel spike detection algorithm, named Precise Timing Spike Detection (PTSD), aimed at (i) reducing the number of false positives and false negatives, in order to optimize the rate code, and (ii) improving the time precision of the identified spike, in order to optimize the spike timing. The PTSD algorithm considers consecutive portions of the signal and looks for the Relative Maximum/Minimum whose peak-to-peak amplitude is above a defined differential threshold and responds to specific requirements. To validate the algorithm, the presented spike detection has been compared with other methods either commercially available or proposed in the literature by using two benchmarking procedures: (i) visual inspection by a group of experts of a portion of signal recorded from a rat cortical culture and (ii) detection of the spikes generated by a realistic neuronal network model. In both cases our algorithm produced the best performances in terms of efficiency and precision. The ROC curve analysis further proved that the best results are reached by the application of the PTSD.

Large-Scale, High-Resolution Data Acquisition System for Extracellular Recording of Electrophysiological Activity

A platform for high spatial and temporal resolution electrophysiological recordings of in vitro electrogenic cell cultures handling 4096 electrodes at a full frame rate of 8 kHz is presented and validated by means of cardiomyocyte cultures. Based on an active pixel sensor device implementing an array of metallic electrodes, the system provides acquisitions at spatial resolutions of 42 mum on an active area of 2.67 mm times 2.67 mm, and in the zooming mode, temporal resolutions down to 8 mus on 64 randomly selected electrodes. The low-noise performances of the integrated amplifier (11 muVrms) combined with a hardware implementation inspired by image/video processing concepts enable high-resolution acquisitions with real-time preprocessing capabilities adapted to the handling of the large amount of acquired data.

Modeling H/sup +/-sensitive FETs with SPICE

A generalized physical model including two kinds of binding sites is presented on H/sup +/-sensitive ISFET devices. The model results in a set of equations which is introduced into a modified version of the electronic circuit simulation program SPICE. In this way, the effects induced on the device performances by varying several physico-chemical parameters are analyzed. The slope of V/sub out/ versus pH curves is predicted for SiO/sub 2/-, Al/sub 2/O/sub 3/-, and Si/sub 3/N/sub 4/-gate ISFETs. The model is then used to predict the behavior of a hypothetical, partially pH-insensitive (REFET) structure. Finally, the model is utilized to fit the slow response of the Al/sub 2/O/sub 3/-gate ISFET to a pH stop. >

Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals

Neuronal assemblies within the nervous system produce electrical activity that can be recorded in terms of action potential patterns. Such patterns provide a sensitive endpoint to detect effects of a variety of chemical and physical perturbations. They are a function of synaptic changes and do not necessarily involve structural alterations. In vitro neuronal networks (NNs) grown on micro-electrode arrays (MEAs) respond to neuroactive substances as well as the in vivo brain. As such, they constitute a valuable tool for investigating changes in the electrophysiological activity of the neurons in response to chemical exposures. However, the reproducibility of NN responses to chemical exposure has not been systematically documented. To this purpose six independent laboratories (in Europe and in USA) evaluated the response to the same pharmacological compounds (Fluoxetine, Muscimol, and Verapamil) in primary neuronal cultures. Common standardization principles and acceptance criteria for the quality of the cultures have been established to compare the obtained results. These studies involved more than 100 experiments before the final conclusions have been drawn that MEA technology has a potential for standard in vitro neurotoxicity/neuropharmacology evaluation. The obtained results show good intra- and inter-laboratory reproducibility of the responses. The consistent inhibitory effects of the compounds were observed in all the laboratories with the 50% Inhibiting Concentrations (IC50s) ranging from: (mean ± SEM, in μM) 1.53 ± 0.17 to 5.4 ± 0.7 (n = 35) for Fluoxetine, 0.16 ± 0.03 to 0.38 ± 0.16 μM (n = 35) for Muscimol, and 2.68 ± 0.32 to 5.23 ± 1.7 (n = 32) for Verapamil. The outcome of this study indicates that the MEA approach is a robust tool leading to reproducible results. The future direction will be to extend the set of testing compounds and to propose the MEA approach as a standard screen for identification and prioritization of chemicals with neurotoxicity potential.