Dataset Information

CMU Array: A 3D nanoprinted, fully customizable high-density microelectrode array platform.

ABSTRACT: Microelectrode arrays provide the means to record electrophysiological activity critical to brain research. Despite its fundamental role, there are no means to customize electrode layouts to address specific experimental or clinical needs. Moreover, current electrodes demonstrate substantial limitations in coverage, fragility, and expense. Using a 3D nanoparticle printing approach that overcomes these limitations, we demonstrate the first in vivo recordings from electrodes that make use of the flexibility of the 3D printing process. The customizable and physically robust 3D multi-electrode devices feature high electrode densities (2600 channels/cm² of footprint) with minimal gross tissue damage and excellent signal-to-noise ratio. This fabrication methodology also allows flexible reconfiguration consisting of different individual shank lengths and layouts, with low overall channel impedances. This is achieved, in part, via custom 3D printed multilayer circuit boards, a fabrication advancement itself that can support several biomedical device possibilities. This effective device design enables both targeted and large-scale recording of electrical signals throughout the brain.

SUBMITTER: Saleh MS

PROVIDER: S-EPMC9534502 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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CMU Array: A 3D nanoprinted, fully customizable high-density microelectrode array platform.

Saleh Mohammad Sadeq MS Ritchie Sandra M SM Nicholas Mark A MA Gordon Hailey L HL Hu Chunshan C Jahan Sanjida S Yuan Bin B Bezbaruah Rriddhiman R Reddy Jay W JW Ahmed Zabir Z Chamanzar Maysamreza M Yttri Eric A EA Panat Rahul P RP

Science advances 20221005 40

Microelectrode arrays provide the means to record electrophysiological activity critical to brain research. Despite its fundamental role, there are no means to customize electrode layouts to address specific experimental or clinical needs. Moreover, current electrodes demonstrate substantial limitations in coverage, fragility, and expense. Using a 3D nanoparticle printing approach that overcomes these limitations, we demonstrate the first in vivo recordings from electrodes that make use of the f ...[more]

PMID: 36197979

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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.

Dataset Information

CMU Array: A 3D nanoprinted, fully customizable high-density microelectrode array platform.

Publications

CMU Array: A 3D nanoprinted, fully customizable high-density microelectrode array platform.

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