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Three-micrometer-diameter needle electrode with an amplifier for extracellular in vivo recordings.

ABSTRACT: Microscale needle-electrode devices offer neuronal signal recording capability in brain tissue; however, using needles of smaller geometry to minimize tissue damage causes degradation of electrical properties, including high electrical impedance and low signal-to-noise ratio (SNR) recording. We overcome these limitations using a device assembly technique that uses a single needle-topped amplifier package, called STACK, within a device of ∼1 × 1 mm² Based on silicon (Si) growth technology, a <3-µm-tip-diameter, 400-µm-length needle electrode was fabricated on a Si block as the module. The high electrical impedance characteristics of the needle electrode were improved by stacking it on the other module of the amplifier. The STACK device exhibited a voltage gain of >0.98 (-0.175 dB), enabling recording of the local field potential and action potentials from the mouse brain in vivo with an improved SNR of 6.2. Additionally, the device allowed us to use a Bluetooth module to demonstrate wireless recording of these neuronal signals; the chronic experiment was also conducted using STACK-implanted mice.

SUBMITTER: Kita Y

PROVIDER: S-EPMC8072214 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Three-micrometer-diameter needle electrode with an amplifier for extracellular in vivo recordings.

Kita Yuto Y Tsuruhara Shuhei S Kubo Hiroshi H Yamashita Koji K Seikoba Yu Y Idogawa Shinnosuke S Sawahata Hirohito H Yamagiwa Shota S Leong Xian Long Angela XLA Numano Rika R Koida Kowa K Kawano Takeshi T

Proceedings of the National Academy of Sciences of the United States of America 20210401 16

Microscale needle-electrode devices offer neuronal signal recording capability in brain tissue; however, using needles of smaller geometry to minimize tissue damage causes degradation of electrical properties, including high electrical impedance and low signal-to-noise ratio (SNR) recording. We overcome these limitations using a device assembly technique that uses a single needle-topped amplifier package, called STACK, within a device of ∼1 × 1 mm<sup>2</sup> Based on silicon (Si) growth technol ...[more]

PMID: 33846241

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Project description:It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

Dataset Information

Three-micrometer-diameter needle electrode with an amplifier for extracellular in vivo recordings.

Publications

Three-micrometer-diameter needle electrode with an amplifier for extracellular in vivo recordings.

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