Dataset Information

Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings.

ABSTRACT: The numerous ?-aminobutyric acid type A receptor (GABAAR) subtypes are differentially expressed and mediate distinct functions at neuronal level. In this study we have investigated GABAAR-mediated modulation of the spontaneous activity patterns of primary neuronal networks from murine frontal cortex by characterizing the effects induced by a wide selection of pharmacological tools at a plethora of activity parameters in microelectrode array (MEA) recordings. The basic characteristics of the primary cortical neurons used in the recordings were studied in some detail, and the expression levels of various GABAAR subunits were investigated by western blotting and RT-qPCR. In the MEA recordings, the pan-GABAAR agonist muscimol and the GABABR agonist baclofen were observed to mediate phenotypically distinct changes in cortical network activity. Selective augmentation of ??? GABAAR signaling by diazepam and of ?-containing GABAAR (?-GABAAR) signaling by DS1 produced pronounced changes in the majority of the activity parameters, both drugs mediating similar patterns of activity changes as muscimol. The apparent importance of ?-GABAAR signaling for network activity was largely corroborated by the effects induced by the functionally selective ?-GABAAR agonists THIP and Thio-THIP, whereas the ?-GABAAR selective potentiator DS2 only mediated modest effects on network activity, even when co-applied with low THIP concentrations. Interestingly, diazepam exhibited dramatically right-shifted concentration-response relationships at many of the activity parameters when co-applied with a trace concentration of DS1 compared to when applied alone. In contrast, the potencies and efficacies displayed by DS1 at the networks were not substantially altered by the concomitant presence of diazepam. In conclusion, the holistic nature of the information extractable from the MEA recordings offers interesting insights into the contributions of various GABAAR subtypes/subgroups to cortical network activity and the putative functional interplay between these receptors in these neurons.

SUBMITTER: Bader BM

PROVIDER: S-EPMC5640229 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings.

Bader Benjamin M BM Steder Anne A Klein Anders Bue AB Frølund Bente B Schroeder Olaf H U OHU Jensen Anders A AA

PloS one 20171013 10

The numerous γ-aminobutyric acid type A receptor (GABAAR) subtypes are differentially expressed and mediate distinct functions at neuronal level. In this study we have investigated GABAAR-mediated modulation of the spontaneous activity patterns of primary neuronal networks from murine frontal cortex by characterizing the effects induced by a wide selection of pharmacological tools at a plethora of activity parameters in microelectrode array (MEA) recordings. The basic characteristics of the prim ...[more]

PMID: 29028808

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Project description: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.

Project description:OBJECTIVE:Functional electrical stimulation (FES) is a promising technology for restoring movement to paralyzed limbs. Intracortical brain-computer interfaces (iBCIs) have enabled intuitive control over virtual and robotic movements, and more recently over upper extremity FES neuroprostheses. However, electrical stimulation of muscles creates artifacts in intracortical microelectrode recordings that could degrade iBCI performance. Here, we investigate methods for reducing the cortically recorded artifacts that result from peripheral electrical stimulation. APPROACH:One participant in the BrainGate2 pilot clinical trial had two intracortical microelectrode arrays placed in the motor cortex, and thirty-six stimulating intramuscular electrodes placed in the muscles of the contralateral limb. We characterized intracortically recorded electrical artifacts during both intramuscular and surface stimulation. We compared the performance of three artifact reduction methods: blanking, common average reference (CAR) and linear regression reference (LRR), which creates channel-specific reference signals, composed of weighted sums of other channels. MAIN RESULTS:Electrical artifacts resulting from surface stimulation were 175??×??larger than baseline neural recordings (which were 110 µV peak-to-peak), while intramuscular stimulation artifacts were only 4??×??larger. The artifact waveforms were highly consistent across electrodes within each array. Application of LRR reduced artifact magnitudes to less than 10 µV and largely preserved the original neural feature values used for decoding. Unmitigated stimulation artifacts decreased iBCI decoding performance, but performance was almost completely recovered using LRR, which outperformed CAR and blanking and extracted useful neural information during stimulation artifact periods. SIGNIFICANCE:The LRR method was effective at reducing electrical artifacts resulting from both intramuscular and surface FES, and almost completely restored iBCI decoding performance (>90% recovery for surface stimulation and full recovery for intramuscular stimulation). The results demonstrate that FES-induced artifacts can be easily mitigated in FES??+??iBCI systems by using LRR for artifact reduction, and suggest that the LRR method may also be useful in other noise reduction applications.

Dataset Information

Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings.

Publications

Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings.

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