High-channel-count, high-density microelectrode array for closed-loop investigation of neuronal networks

2015 ◽  
Author(s):  
David Tsai ◽  
Esha John ◽  
Tarun Chari ◽  
Rafael Yuste ◽  
Kenneth Shepard
Keyword(s):  
Neuronal Networks ◽  
Microelectrode Array ◽  
Closed Loop ◽  
High Density

An integrated microfluidic/microelectrode array for the study of activity-dependent intracellular dynamics in neuronal networks

Lab on a Chip ◽  
2018 ◽  
Vol 18 (22) ◽  
pp. 3425-3435 ◽  
Author(s):  
Eve Moutaux ◽  
Benoit Charlot ◽  
Aurélie Genoux ◽  
Frédéric Saudou ◽  
Maxime Cazorla
Keyword(s):  
Neuronal Activity ◽  
Neuronal Networks ◽  
Microelectrode Array ◽  
Activity Dependent

A microfluidics/MEA platform was developed to control neuronal activity while imaging intracellular dynamics within reconstituted neuronal networks.

scholarly journals Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinyue Yuan ◽  
Manuel Schröter ◽  
Marie Engelene J. Obien ◽  
Michele Fiscella ◽  
Wei Gong ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Microelectrode Array ◽  
Cell Activity ◽  
Live Cell ◽  
High Signal ◽  
Neuronal Cultures ◽  
Label Free ◽  
Fluorescent Indicators ◽  
Analysis Platform

AbstractChronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.

scholarly journals Deuterated Glutamate-Mediated Neuronal Activity on Micro-Electrode Arrays

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 830
Author(s):  
Wataru Minoshima ◽  
Kyoko Masui ◽  
Tomomi Tani ◽  
Yasunori Nawa ◽  
Satoshi Fujita ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Neuronal Activity ◽  
Hippocampal Neurons ◽  
Neuronal Networks ◽  
Microelectrode Array ◽  
Mammalian Brain ◽  
Electrode Arrays ◽  
Physiological Properties ◽  
Micro Electrode Arrays

The excitatory synaptic transmission is mediated by glutamate (GLU) in neuronal networks of the mammalian brain. In addition to the synaptic GLU, extra-synaptic GLU is known to modulate the neuronal activity. In neuronal networks, GLU uptake is an important role of neurons and glial cells for lowering the concentration of extracellular GLU and to avoid the excitotoxicity. Monitoring the spatial distribution of intracellular GLU is important to study the uptake of GLU, but the approach has been hampered by the absence of appropriate GLU analogs that report the localization of GLU. Deuterium-labeled glutamate (GLU-D) is a promising tracer for monitoring the intracellular concentration of glutamate, but physiological properties of GLU-D have not been studied. Here we study the effects of extracellular GLU-D for the neuronal activity by using primary cultured rat hippocampal neurons that form neuronal networks on microelectrode array. The frequency of firing in the spontaneous activity of neurons increased with the increasing concentration of extracellular GLU-D. The frequency of synchronized burst activity in neurons increased similarly as we observed in the spontaneous activity. These changes of the neuronal activity with extracellular GLU-D were suppressed by antagonists of glutamate receptors. These results suggest that GLU-D can be used as an analog of GLU with equivalent effects for facilitating the neuronal activity. We anticipate GLU-D developing as a promising analog of GLU for studying the dynamics of glutamate during neuronal activity.

scholarly journals Applicability of independent component analysis on high-density microelectrode array recordings

2012 ◽  
Vol 108 (1) ◽  
pp. 334-348 ◽  
Author(s):  
David Jäckel ◽  
Urs Frey ◽  
Michele Fiscella ◽  
Felix Franke ◽  
Andreas Hierlemann
Keyword(s):  
Microelectrode Array ◽  
Simulated Data ◽  
Complementary Metal Oxide Semiconductor ◽  
Simultaneous Recording ◽  
High Density ◽  
Independent Component ◽  
Oxide Semiconductor ◽  
Spike Sorting ◽  
Spatiotemporal Resolution ◽  
Source Signals

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

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