Revealing neuronal function through microelectrode array recordings.
- Authors
- Obien, Marie Engelene J; Deligkaris, Kosmas; Bullmann, Torsten; Bakkum, Douglas J; Frey, Urs
- Year
- 2014
- Journal
- Frontiers in neuroscience
- PMID
- 25610364
- DOI
- 10.3389/fnins.2014.00423
- PMCID
- PMC4285113
Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.
Typical electrophysiological methods. (A) Macroscopic recording via electroencephalography (EEG) and mesoscopic recording through electrocorticography (ECoG) and implantable electrodes, with the corresponding representative waveforms recorded in a patient with drug-resistant epilepsy. The measured signal amplitudes are larger for ECoG and implanted electrodes (local field potential or LFP recording) compared to EEG. The waveforms for EEG, ECoG, and implant are modified with permission from BuzsΓ‘ki et al. (2012). (B) Mesoscopic and microscopic recording using a tetrode (extracellular) and a glass micropipette (intracellular), respectively. The fast EAP extracted from the raw tetrode recordings correlate with the intracellular APs recorded from a pyramidal cell. (Left) Illustration of cells across cortical layers modified with permission from BuzsΓ‘ki et al. (2012). (Right) Signals for simultaneous extracellular and intracellular recordings modified with permission from Henze et al. (2000).
LLM interpretation
This figure consists of diagrams and representative waveforms illustrating different electrophysiological recording scales. Panel A shows a schematic of EEG, ECoG, and implantable electrodes in a human brain, with corresponding waveforms demonstrating that signal amplitudes increase from EEG to ECoG and implant LFP. Panel B displays a cortical layer diagram with extracellular (tetrode) and intracellular (micropipette) probes, alongside waveforms showing the correlation between extracted extracellular action potentials (EAP) and intracellular action potentials.
High-resolution mapping of spontaneous Purkinje cell activity using HDMEAs. (AβE) HDMEA recordings from an acute slice preparation of the caudal half of the cerebellar vermis. (A) Activity map of the detectable spike activity in the recording area. Small dots correspond to the electrodes used for recording (~30% of the available electrodes). Events exceeding a threshold of Β±36 ΞΌV were used to calculate the color-coded event rate. Scale bar: 0.3 mm. (B) Close-up of a region with high activity delimited in (A). All units identified by spike sorting are marked, i.e., the somatic region is blue and the dendritic region is red. Scale bar: 0.1 mm. (C) Schematic of the basic cellular structures in the cerebellar slice (Gray, 1918). Scale bar: 0.1 mm. ML, molecular layer; PCL, Purkinje cell layer; GL, granular layer; CF, climbing fiber; MF, mossy fiber; PF, parallel fiber; PC, Purkinje cell; GgC, Golgi cell; SC, stellate cell; BC, basket cell. (D) Footprint of a PC selected from the region shown in (B). Scale bar: vertical is 200 ΞΌV, horizontal is 1.9 ms. (E) Current source density (CSD) analysis for the cell shown in (D) at several points in time (green: sink; yellow: source). The sink moves from the soma at 0.4 ms to the proximal dendrites at 0.6 ms and covers the dendritic area, while the soma repolarizes. Frequency band: 180 Hzβ3.5 kHz. (FβH) Matching simulated and measured EAP footprints. (F) Comparison of the recorded average single-unit spikes (black traces) and the spikes calculated from a compartment-model simulation of a PC (green traces). Scale bar: vertical is 100 ΞΌV, horizontal is 1.9 ms. (G) Illustration of the position and orientation of the simulated PC, with the center of the soma located [blue diamond in (F)] 40 ΞΌm above the chip surface. (H) Simulated potential on the chip surface along a line parallel to the soma-dendrite axis [dashed blue line in (F,G)] during the spike evolution at 0.1 ms intervals. The black and white dots on the potential line of maximal amplitude (bold blue line) represent the HDMEA spatial resolution (18 ΞΌm pitch). Significant spatial undersampling of the potential distribution curve can be observed by reducing the lateral spatial resolution by 50% (black dots only, pitch 36 ΞΌm), especially for the largest negative peak. All panels and descriptions adapted with permission from Frey et al. (2009a).
LLM interpretation
This figure presents high-resolution mapping of Purkinje cell activity using HDMEAs across several panels. It includes a color-coded activity map of spike rates (A), a close-up of identified somatic (blue) and dendritic (red) regions (B), and a schematic of cerebellar slice anatomy (C). The figure further displays the footprint of a selected Purkinje cell (D), a time-resolved current source density analysis showing sink/source movement (E), and a comparison between recorded and simulated extracellular action potential footprints (FβH).
Identification of retinal ganglion cell receptive fields using HDMEAs. (AβE) Characterization and analysis of HDMEA recordings from defined populations of mouse retinal ganglion cells (RGCs), adapted with permission from Fiscella et al. (2012). (A) Each trace shows the average (thick black lines) of the 959 superimposed EAPs (gray lines). The electrode locations are indicated in (B). The propagation speed of the spike was calculated to be 0.7 m/s. (B) Footprint of an RGC over an area of 0.025 mm2. The highest peak-to-peak amplitude is shown by the thick dark waveform. (CβE) Physiological response of RGCs. Left panel: RGC footprint on a recording block of the HDMEA. The yellow square indicates the location of the light stimulus, with the gray squares indicating the center of the stimulus at four positions. Middle panel: Raster plots corresponding to four stimulation locations indicated in the left panel. Each dot corresponds to a single EAP. Each raster plot shows the response to five repetitions of the same stimulus. The firing rate of the RGC (averaged from five responses) is indicated below. Right panel top: Polar plot showing the responses of the RGC to motion of a bar in 8 directions at 45Β° radial intervals. Right panel bottom: Inter-spike interval distribution showing the time intervals between consecutive spikes. (C) Blue = ON RGC. (D) Red = OFF RGC. (E) Green = ON-OFF RGC. (F) Classification of RGC types and receptive fields at single cone resolution, adapted with permission from Field et al. (2010). The RGCs were recorded simultaneously and classified using the responses to white noise stimuli. Top middle panel: Receptive field radius vs. the first principal component of the response time course. The clusters reveal different RGC types. Surrounding panels: Identified RGC types highlighted at the top middle panel. The RGCs are stimulated with fine-grained white noise to reveal single cone receptive fields. Scale bars: 50 ΞΌm.
LLM interpretation
This figure presents the characterization of mouse retinal ganglion cell (RGC) receptive fields using high-density microelectrode arrays (HDMEAs). Panels AβE show extracellular action potential traces, spatial footprints, raster plots of light-stimulus responses, polar plots of motion sensitivity, and inter-spike interval distributions for ON (blue), OFF (red), and ON-OFF (green) RGCs. Panel F displays a scatter plot clustering RGC types by receptive field radius and time course projection, accompanied by spatial maps of single-cone resolution receptive fields for specific types, including midget, parasol, and small bistratified cells.
Imaging axonal signal propagation using HDMEAs. (AβC) Axonal propagation of a cultured neuron on an HDMEA, adapted with permission from Bakkum et al. (2013). (A) Live image of a neuron at 21 DIV transfected with red fluorescent protein (RFP). The axon is highlighted. (B) Illustration of the distributed stimulation method. The crosshair represents the location of the βsomaticβ AP observed while stimulating different electrodes represented by colored dots (color represent the median latency until AP detection, where light gray corresponds to electrodes that did not evoke an AP). The small dots represent the location of the HDMEA electrodes. Scale bar, 40 ΞΌm. (C) Illustration of the single-site stimulation method. The red crosshair represents the stimulated electrode. The colored dots represent the latencies of detected APs with respect to the largest voltage signal indicated by the arrow. Scale bar, 40 ΞΌm. (D) Axonal propagation of an RGC from rabbit retina, adapted with permission from Zeck et al. (2011). Consecutive electrical images of the EAP propagation allow for the calculation of axonal conduction velocity. (a) Image of a somatic AP (blue spot in the first window) propagating along the proximal axon. (b) Image of a biphasic spike recorded from an axon. (c) Plot indicating the distance traveled of the AP in time. Open symbols represent data calculated from recordings at 16.4 kHz; closed symbols are recordings at 8.2 kHz.
LLM interpretation
This figure demonstrates axonal signal propagation using High-Density Microelectrode Arrays (HDMEAs). Panels AβC show a cultured neuron with its soma and axon, utilizing color-coded dots to represent action potential (AP) latencies under distributed (B) and single-site (C) stimulation methods. Panel D includes a series of electrical images (a, b) showing the spatiotemporal propagation of an AP in a rabbit retinal ganglion cell, paired with a scatter plot (c) showing a linear increase in distance traveled over time for two different recording frequencies.
Localization of single neurons. (A) Spike current source density (sCSD) method by SomogyvΓ‘ri et al. (2012), figure modified with permission. The experimental setup is shown on the left, where the neuron is oriented at a distance d parallel to the in vivo MEA. The highest amplitude comes from the current sources at the soma of the neuron (sink) and is detected by multiple electrodes. The forward solution at d is given by the T(d) matrix, which transforms the CSD on the neuron to the EAP detected by the MEA. The EAPs are shown in the voltage traces per electrode, where one spike is plotted as a color map, indicating the spatial EAP pattern in time. The sCSD obtained from the EAP signals by inverse solution Tβ1(dopt) is shown on the right. The EAP spatio-temporal map is transformed into a series of normalized CSD distributions [I(d)] with different d-values. Localization is done by solving for dopt. The optimum d (dopt) is chosen as the value where I(d) is the most spike-like, i.e., similar to the normalized amplitude of the EAP during the whole duration of the spike. Thus, the EAP and sCSD color maps are similar. (BβD) Localization of simulated neurons using simplified line model by Delgado Ruz and Schultz (2014), figures adapted with permission. (B) The simulated neurons are CA1 pyramidal, L2/3 pyramidal, double bouquet or DB (not shown), NPY interneurons, and PV interneuron. Localization depends on the location of the sodium trough, which corresponds to the moment when currents are concentrated near the soma. As shown by the color map embedded on the neuron morphologies, the sodium trough (red) is displaced from the soma for NPY due to the contribution of the dendritic arbor and axon, leading to higher localization error along the Y axis shown in (D). (C) Localization results for CA1, where the errors along XβZ axes remained low for neuron-electrode distances under 35 ΞΌm and increased thereafter, especially along the Z axis. (D) The localization errors were not similar for all simulated neurons. The differences in morphology and electrophysiology cause the errors, although the maximum EAP (location of sodium trough) is more or less confined to the perisomatic area.
LLM interpretation
This figure illustrates a method for localizing single neurons using spike current source density (sCSD). Panel A shows a schematic of the experimental setup, including a neuron, a multi-electrode array (MEA), and the transformation of extracellular potentials into a spatio-temporal sCSD map. Panels BβD present simulation results, featuring morphology maps of different neuron types (CA1, NPY, PV, L23p) and line plots and box plots showing localization errors across X, Y, and Z axes relative to the distance from the soma.
Ion channel density estimation. Adapted from Gold et al. (2006). (A) The extracellular action potentials (EAPs) solved in a grid from the multicompartmental model of a CA1 pyramidal neuron. The dotted black line indicates the tip of the electrode used to measure the EAPs. (B) Enlarged image of the EAP at the electrode tip. Location is indicated by the white dotted line in (A). Solid line in the plot corresponds to the simulated EAP, which is superimposed with the recorded EAP shown as dotted line. (C) Comparison of the simulated intracellular signal (solid line) at the proximal apical trunk to the intracellular recording (dotted line). (D) First column: The details of the intracellular signal simulation for each compartment. White solid lines in (A) indicate the locations of the compartments. Second column: The simulated membrane currents in the same compartments as the first column. The net membrane current across the soma and proximal dendrites best estimates the EAP waveform. Third column: Membrane current components in terms of Na+, K+, and mixed-ion capacitive current. Last column: Conductivity densities of the A, C, D, K, and M type K+ currents. For further details, see Gold et al. (2006).
LLM interpretation
This figure presents a multicompartmental model of a CA1 pyramidal neuron used for ion channel density estimation. Panel (A) shows a spatial grid of simulated extracellular action potentials (EAPs) across the neuron's morphology, with Panel (B) comparing a simulated EAP to a recorded one at the electrode tip and Panel (C) comparing simulated and recorded intracellular signals. Panel (D) provides a detailed breakdown by compartment (e.g., soma, proximal apical trunk) of the membrane voltage, total membrane current, individual ionic current components ($\text{Na}^+$, $\text{K}^+$, and capacitive), and the corresponding conductivity densities.
Device comparison. MEA comparison with respect to (A) electrode density and total sensing area, and (B) parallel recording channel count and noise level. (A) For devices with a regular sensor pitch, such as most in vitro MEA devices, the total area is calculated as number of electrodes times the pixel area. For all devices, the number of electrode times the inverse of the electrode density matches the total area. The light gray lines illustrate the number of electrodes. (B) The noise values shown are approximated RMS values stated in the respective citations. The conditions under which these measurements were taken usually differ significantly (such as noise bandwidth, in- or exclusion of electrode noise, inclusion of ADC quantization noise, etc.). Therefore, this graph only serves as a rough comparison. The waveforms to illustrate the noise levels are simulated and have a spectrum typical for MEA recordings. The simulated spikes are typical spikes for acute brain slice measurements recorded with microelectrodes. The recorded amplitudes may vary significantly depending on preparation and sensor characteristics. See Footnotes:3,4,5,6,7.
LLM interpretation
This figure consists of two scatter plots comparing different Microelectrode Array (MEA) devices. Panel (A) plots sensing area ($\text{mm}^2$) against electrode density ($\text{mm}^{-2}$) on logarithmic scales, with data points categorized by device type (Type C-E) and environment (*in vitro* vs. *in vivo*). Panel (B) plots the inverse of noise ($1/\text{noise}$ in $1/\mu\text{V}_{\text{rms}}$) against the number of parallel readout channels, with different markers representing device types B through E.
Array architectures. This table summarizes and classifies the different architectures that are typically used for MEAs. Advantages, disadvantages are stated and representative selected references given. (A,B) Fixed wiring. (A) Electrodes are directly connected to signal pads with no active circuitry. (B) Electrodes are directly connected to on-chip active circuitry for signal conditioning. (CβE) Multiplexed arrays. (C) Signals are multiplexed to the signal pads via column, row addressing in static mode. (D) More flexible addressing is achieved by adding more routing resources within the array in the switch-matrix mode. (E) All electrodes can be sampled at fast speeds in full-frame readout implemented in active pixel sensor (APS) MEAs.
LLM interpretation
This figure is a summary table classifying five different Multi-Electrode Array (MEA) architectures (AβE), featuring schematic diagrams and comparative text. The table categorizes the architectures into "Fixed wiring" (A, B) and "Multiplexed arrays" (C, D, E), detailing the specific advantages, disadvantages, and representative references for each. Each schematic uses a color-coded legend to identify components such as front-end amplifiers, selected/inactive electrodes, signal pads, power supply pads, switches, and local memory.
Stimulation capability of high-resolution CMOS-based MEA. (A) Examples of evoked spikes detected at three sites (columns) along the same axon. The top row shows individual raw traces, and the other rows show traces averaged as indicated. Scale bars, 1 ms horizontal, 10 ΞΌV vertical. (B) The amount of averaging necessary to detect a spike with a given height (0.5β3 Ο) with respect to the detection threshold. (C) Left: A raw voltage trace recorded at an electrode neighboring a stimulation electrode saturated for about 4 ms (flat line). Right: A raw voltage trace recorded at an electrode located 1.46 mm away from a stimulation electrode did not saturate. (D) The duration of a saturated signal occurring after stimuli is plotted vs. distance from the stimulation electrode (mean Β± s.e.m.; N = 18 stimulation electrodes from five CMOS-based MEAs). Stimuli consisted of biphasic voltage pulses between 100 and 200 ms duration per phase and between Β± 400 and 800 mV amplitude. (E) Locations of stimulation electrodes that directly evoked (black boxes) or did not evoke (empty or filled gray boxes) APs detected at a soma located ~890 ΞΌm away. The line arrow indicates the orthodromic propagation direction. Scale bar, 20 ΞΌm. (F) Voltage traces of somatic APs elicited by biphasic voltage stimuli. Traces in response to eight stimuli are overlaid for each of three stimulation magnitudes (indicated at the top), plotted for all effective (black) and four ineffective stimulation sites (gray at the bottom). Stimulation electrode locations are represented as numbered boxes in (E). Scale bar, 200 ΞΌV. All panels and description adapted with permission from Bakkum et al. (2013).
LLM interpretation
This figure evaluates the stimulation capabilities of a high-resolution CMOS-based MEA through several panels. It includes raw and averaged voltage traces of evoked spikes (A), a line graph showing the number of trials required for spike detection relative to the detection threshold (B), and voltage traces comparing saturated signals near stimulation sites to distant action potentials (C). Additionally, a scatter plot shows the duration of signal saturation decreasing as distance from the stimulation electrode increases (D), while a spatial map (E) and corresponding overlaid voltage traces (F) identify which stimulation sites and magnitudes successfully elicited somatic action potentials.
CMOS-based in vitro MEAs. CMOS-based in vitro MEAs, their key specifications and references to biological applications for recording and stimulation are listed in this table. The application list includes only one representative citation for each type of preparation. The specification for each device are taken from the reference listed on top and may differ for other versions of the device.
LLM interpretation
This figure is a summary table comparing various CMOS-based in vitro microelectrode arrays (MEAs), categorized by type (Types B, C, D, and E). For each device, the table provides a micrograph image, key technical specifications (such as technology node, chip area, electrode count, and power), and references for published recording and stimulation applications. The table includes a footer defining abbreviations for the animal species and biological preparations used in the cited studies.
MEA stimulation and recording system diagram with the noise sources. The neuron is stimulated by the pulses or waveform generated digitally through the MEA. The response of the neuron, typically an action potential, is transformed by different parameters across the components of the MEA toward the recorded signal.
LLM interpretation
This is a schematic diagram illustrating the MEA stimulation and recording system and its associated noise sources. The workflow shows a bidirectional path between a neuron and recorded signals, passing through a volume conductor, an electrode, and hardware components (amplification, filtering, ADC, etc.). Three yellow boxes identify external noise sources: biological noise/interference, electrode-electrolyte interface noise, and device noise.
MEA neuron-electrode interface. (A) The classic point or area contact model derived from Fromherz (2003). The cell membrane is represented with an equivalent model based on the Hodgkin-Huxley model of the squid axon (Hodgkin and Huxley, 1952). CM represents the capacitance across the neuronal membrane, i.e., the lipid bilayer. The voltage-gated ion channels (K for potassium and Na for sodium) are represented by non-linear conductances, gK and gNa, and the leak is shown as a linear conductance, gL. The reversal potentials that drive the flow of ions are represented by EK, ENa, and EL. The ion flow is shown by IK, INa, IL, and IC. The other elements are described in the text. Vrec is the recorded voltage signal. Typical IAP and EAP recordings are shown. The location of the scissors indicates where the βcutβ can be made to separate the neuron-electrode interface into two parts. (B) Generalized neuron-electrode interface separating the problem into two parts. UpperββFluidβ-side: The potential at the electrode sites can be solved using the volume conductor theory. The MEA surface is assumed to be an insulator such that the method of images can be applied on Coulomb's law to solve the potential at any point on the MEA surface. The neuron-electrode distance influences the signal amplitude measured at the electrodes. High spatial resolution allows for recording at several locations of a single neuron, with large negative spikes located at the perisomatic area and positive spikes at the dendritic area, i.e., return current. LowerββMetalβ-side: The voltage measured at the electrode is shaped by the electrical parameters of the electrode-electrolyte interface, represented by Zβ²e as the effective electrode impedance and Zβ²a as the effective input impedance. This model is derived from Robinson (1968), Nelson et al. (2008), Hierlemann et al. (2011).
LLM interpretation
This figure consists of two schematic diagrams illustrating the neuron-electrode interface. Panel (A) shows a circuit model based on the Hodgkin-Huxley model, detailing membrane capacitance ($C_M$), ion conductances ($g_K, g_{Na}, g_L$), and the electrical path from the neuron to the recorded voltage ($V_{rec}$). Panel (B) presents a generalized model separating the "Fluid-side" (showing potential contours around a neuron and corresponding signal spikes at different electrode locations) from the "Metal-side" (represented by an equivalent circuit with effective electrode impedance $Z'_e$ and input impedance $Z'_a$).
Neuronal culture studies using MEAs. (A,B) Combination of MEAs with immunostaining and microscopy to analyze the relationship between the development of synapses and electrical activity of neurons, adapted with permission from Ito et al. (2013). (A) Plot showing the number of synapses along the neuronal dendrites in a long-term primary culture. The glutamatergic (red) and GABAergic (green) synapses along the dendrites of neurons were obtained by immunostaining from cultures at 7β35 days in vitro (DIV). The number of synapses at the dendrites continuously increased for 3 weeks and saturated afterwards. The same is true for synapses at the soma (not shown), which saturated after 30 DIV. (B) Plotted data from MEA recordings of a long-term culture. A similar pattern is observed from the firing rate and synchronized burst rate measured by a MED64 MEA device from 7 to 35 DIV. Both the firing and burst rates increased until 30 DIV, which eventually saturated afterwards. (C,D) Application of HDMEAs to analyze the functional connectivity of neurons in vitro, adapted with permission from Maccione et al. (2012). Fluorescent images of stained neurons on an HDMEA are shown with arrows indicating the functional connectivity (from whiteβweak to redβstrong) obtained by analyzing spike trains using cross-correlation.
Waves in acute hippocampal slices revealed by MEAs. (AβC) Studying the effect of the delayed rectifier potassium channel Ξ±-subunit Kv1.1 to sharp waves in in vitro hippocampal slices using MEAs, modified with permission from Simeone et al. (2013). (A) Image of a Kcna1-null (knock-out of the gene encoding Kv1.1) hippocampal slice on an MEA. Black squares correspond to the electrodes. The regions of the hippocampus are also indicated. (B) The sharp waves in wild-type (WT) and Kcna1-null hippocampi are initiated in CA3 that spread with similar time-courses. (C) Representative sharp waves from WT and Kcna1-null hippocampi recorded at the location of red boxes in (A). The sharp waves are longer (with ripples) in Kcna1-null compared to WT. Scale bars: horizontal, 50 ms; vertical, 50 ΞΌV except for WT CA3sp (100 ΞΌV), WT CA3sr (200 ΞΌV), KO CA1sp (20 ΞΌV), and WT CA1sr (200 ΞΌV). CA, cornus ammonis; DG, dentate gyrus. (D,E) Studying the effect of deleting synapsin II (Syn II) to the tonic inhibition in mouse hippocampal slices using HDMEAs, adapted with permission from Medrihan et al. (2014). (D) Mean firing rate computed from each electrode from WT and Syn II knock-out hippocampal slices before and after THIP treatment. THIP: (4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridin-3-ol; gaboxadol), a selective agonist of Ξ΄ subunit-containing GABAA receptors. (E) Raster plots showing highly synchronized bursts, x-axis corresponds to time, y-axis corresponds to pixels (electrode). THIP reduced the high frequency bursts in Syn II knock-out hippocampus. Scale bar: 1 min.
LLM interpretation
This figure consists of five panels (AβE) analyzing hippocampal activity using multi-electrode arrays (MEAs). Panels AβC show a hippocampal slice image, propagation maps, and representative waveforms, indicating that sharp waves in *Kcna1*-null slices are longer and contain more ripples than in wild-type (WT) slices. Panels D and E utilize heatmaps of mean firing rates (MFR) and raster plots to show that THIP treatment reduces high-frequency synchronized bursts in *SynII* knock-out slices compared to WT. Labeled regions include CA1, CA3, and the dentate gyrus (DG), with MFR measured in Hz.
| # | Section | Preview |
|---|---|---|
| 60 | Understanding MEA signals β MEA signal flow β Neuron-electrode interface | shunt capacitance to the signal is small (Robinson, 1968; Nelson et al., 2008). HDMEAs require smallβ¦ |
| 61 | Understanding MEA signals β MEA signal flow β Effect of electrode size and density | Sizes of published microelectrodes range from 5 to 50 ΞΌm in diameter (Kim et al., 2014). Largerβ¦ |
| 62 | Understanding MEA signals β MEA signal flow β Effect of electrode size and density | As discussed above, for EAP recording in the 300β3000 Hz frequency band, electrode noise is mostlyβ¦ |
| 63 | Understanding MEA signals β MEA signal flow β Effect of electrode size and density | LFP and EAP recordings from neurons located distant to the electrodes feature lower spatialβ¦ |
| 64 | Understanding MEA signals β MEA signal flow β Effect of electrode size and density | It is therefore important to choose optimal electrode sizes depending on the targeted application.β¦ |
| 65 | Practical application of microelectrode recordings | Here, we provide a brief overview on how to extract relevant information from distorted, convoluted,β¦ |
| 66 | Practical application of microelectrode recordings β MEA signal processing and spike sorting | MEA signal processing usually includes (1) filtering the raw data traces, (2) spike detection, andβ¦ |
| 67 | Practical application of microelectrode recordings β MEA signal processing and spike sorting | First, the raw signal is processed to separate the fast APs from LFP and noise by applying aβ¦ |
| 68 | Practical application of microelectrode recordings β MEA signal processing and spike sorting | Once the signal is filtered, the spikes are detected. Amplitude thresholding is commonly used,β¦ |
| 69 | Practical application of microelectrode recordings β MEA signal processing and spike sorting | After spike detection, spike shapes are grouped according to their spike shape, which is referred toβ¦ |
| 70 | Practical application of microelectrode recordings β MEA signal processing and spike sorting | A number of concerns have been raised regarding the effectiveness of spike sorting. In fact, it isβ¦ |
| 71 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies | MEA recordings have been employed to understand neuronal communication, information encoding,β¦ |
| 72 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β Bursts | Bursts and burst rates of APs in a neuron or across a network of neurons is a common featureβ¦ |
| 73 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β Bursts | Besides single neuron bursting, population-wide synchronous activities are also of interest. Forβ¦ |
| 74 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β MEAs and neuronal cultures | Since Pine reported the first MEA recordings from dissociated neuronal cultures in 1980 (Pine,β¦ |
| 75 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β MEAs and neuronal cultures | More complex neuronal culture analyses can be done using HDMEAs such as burst pattern trackingβ¦ |
| 76 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β MEAs and brain slices | A brain slice is a 3D environment of neurons that can be placed on MEAs to monitor electricalβ¦ |
| 77 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β MEAs and brain slices | have been employed to investigate the disruption of normal network waves and oscillations in theβ¦ |
| 78 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β MEAs and brain slices | Depth recording of EAPs from neurons up to 100 ΞΌm distance from the MEA surface was also shownβ¦ |
| 79 | Practical application of microelectrode recordings β Using MEAs for neuroscience studies β MEAs and brain slices | Aside from acute preparations, MEAs have been used to analyze the brain function using organotypicβ¦ |
| Name | Type |
|---|---|
| Action potential (AP) local | phenotype |
| active device local | drug |
| Aluminum local | drug |
| array local | drug |
| artifact | phenotype |
| artificial cerebrospinal fluid | drug |
| Au mushrooms local | drug |
| Axonal AP propagation local | phenotype |
| Axonal conduction local | phenotype |
| axon initial segment local | anatomy |
| Blue-sensitive cone local | phenotype |
| brain | anatomy |
| Brain State local | phenotype |
| brain tissue | anatomy |
| burst firing | phenotype |
| Bursting local | phenotype |
| Calcium indicator local | drug |
| carbogen local | drug |
| Carbon nanotubes local | drug |
| cardiomyocytes local | drug |
| cell culture dish local | drug |
| Cellulose nitrate coating local | drug |
| Cerebellar slice local | anatomy |
| cerebellum | anatomy |
| chicken | cohort |
| Cone photoreceptor local | phenotype |
| Cone photoreceptors local | anatomy |
| cortex | anatomy |
| delayed rectifier local | gene |
| dendritic structures local | anatomy |
| device local | drug |
| Doped diamond local | drug |
| drug | drug |
| EAP local | phenotype |
| EAPs local | phenotype |
| electrical activity local | phenotype |
| electrical impedance local | drug |
| electric double layer local | drug |
| electrode local | drug |
| Electrode local | drug |
| electrode_impedance local | drug |
| Electrode impedance local | phenotype |
| electrode_noise local | drug |
| electrolyte local | drug |
| electroporation local | drug |
| epilepsy | phenotype |
| Extracellular Action Potentials local | phenotype |
| Extracellular recordings local | drug |
| extracellular space local | anatomy |
| fast ripples local | phenotype |
| fixed wiring array local | drug |
| fluid | drug |
| fluid side local | drug |
| four-point probes method local | drug |
| GABAergic synapses local | phenotype |
| glial cells | anatomy |
| glutamatergic synapses local | phenotype |
| Gold local | drug |
| Gold nanostructures local | drug |
| Graphene local | drug |
| Green-sensitive cone local | phenotype |
| guinea pig | cohort |
| HDMEA local | drug |
| HDMEAs local | drug |
| High-density EAP recordings local | drug |
| High-density microelectrode arrays (HDMEAs) local | drug |
| high frequency oscillations | phenotype |
| hippocampus | anatomy |
| humans | cohort |
| Intracellular Action Potentials local | phenotype |
| intracellular activity local | phenotype |
| in vitro MEA local | drug |
| in vivo neural probes local | drug |
| Ion channel density local | phenotype |
| Ion channel dynamics local | phenotype |
| Iridium oxide local | drug |
| Kcna1 | gene |
| Kelvin sensing local | drug |
| LFP | phenotype |
| LFPs local | phenotype |
| Light stimulation local | drug |
| local field potentials | drug |
| MEA device local | drug |
| MEAs local | drug |
| MEA surface local | drug |
| medium chamber local | drug |
| Memory traces local | phenotype |
| metal | drug |
| metal side local | drug |
| mice | cohort |
| microelectrode local | drug |
| Midget retinal ganglion cell local | phenotype |
| molecular layer | anatomy |
| monkeys | cohort |
| multi-electrode array local | cohort |
| multiplexed array local | drug |
| Na+ | drug |
| networks local | phenotype |
| network waves local | phenotype |
| neural probe local | drug |
| neuronal activity | phenotype |
| neuronal cells local | drug |
| neuronal cells | phenotype |
| neuronal cultures | cohort |
| neurons | phenotype |
| non-metallic electrodes local | drug |
| olfactory bulb | anatomy |
| ONβOFF direction-selective RGC local | phenotype |
| organotypic slice cultures local | cohort |
| oscillations | phenotype |
| Parasol retinal ganglion cell local | phenotype |
| Parkinson's disease | phenotype |
| passive device local | drug |
| Peanut agglutinin local | drug |
| perisomatic area local | anatomy |
| Platinum local | drug |
| Poly(3,4-ethylenedioxythiophene) local | drug |
| polytrode local | drug |
| Population-wide synchronous activities local | phenotype |
| potassium | drug |
| Pt-black local | drug |
| Purkinje cell layer local | anatomy |
| Purkinje cells | anatomy |
| Rabbit retina local | anatomy |
| rabbits local | cohort |
| rats | cohort |
| recording site local | anatomy |
| Red-sensitive cone local | phenotype |
| retina | anatomy |
| Retinal ganglion cell local | phenotype |
| Retinal ganglion cells local | anatomy |
| Silicon local | drug |
| silicon nanowires local | drug |
| Single neuron bursting local | phenotype |
| single neurons local | phenotype |
| sleep | phenotype |
| Slice anchor local | drug |
| Small bistratified retinal ganglion cell local | phenotype |
| snails local | cohort |
| soma local | anatomy |
| Stainless steel local | drug |
| stimulation pulse local | drug |
| Subthreshold Activity local | phenotype |
| suprachiasmatic nucleus | anatomy |
| SYN2 local | gene |
| synapse density local | phenotype |
| synchronized bursts local | phenotype |
| system local | drug |
| THIP | drug |
| tissue | anatomy |
| Titanium nitride local | drug |
| tonic inhibition | phenotype |
| ultra-small electrodes local | drug |
| volume conductor local | drug |
| white matter | anatomy |
| White noise visual stimulation local | drug |
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| The microbiota-gut-brain axis and epilepsy from a multidisciplinary perspective: Clinical evidence and technological solutions for improvement of in vitro preclinical models. | Fusco F et al. | β | 2022 | β |
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| Printing "Smart" Inks of Redox-Responsive Organometallic Polymers on Microelectrode Arrays for Molecular Sensing. | Cirelli M et al. | β | 2019 | β |
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| On-chip, multisite extracellular and intracellular recordings from primary cultured skeletal myotubes. | Rabieh N et al. | β | 2016 | β |
| Optogenetic Mapping of Functional Connectivity in Freely Moving Mice via Insertable Wrapping Electrode Array Beneath the Skull. | Park AH et al. | β | 2016 | β |
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