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高密度碳纤维阵列用于慢性电生理学、快速扫描循环伏安法和相关解剖学。

High density carbon fiber arrays for chronic electrophysiology, fast scan cyclic voltammetry, and correlative anatomy.

机构信息

Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America.

Department of Psychology, University of Michigan, Ann Arbor, MI 48109, United States of America.

出版信息

J Neural Eng. 2020 Oct 15;17(5):056029. doi: 10.1088/1741-2552/abb1f6.

DOI:10.1088/1741-2552/abb1f6
PMID:33055366
Abstract

OBJECTIVE

Multimodal measurements at the neuronal level allow for detailed insight into local circuit function. However, most behavioral studies focus on one or two modalities and are generally limited by the available technology.

APPROACH

Here, we show a combined approach of electrophysiology recordings, chemical sensing, and histological localization of the electrode tips within tissue. The key enabling technology is the underlying use of carbon fiber electrodes, which are small, electrically conductive, and sensitive to dopamine. The carbon fibers were functionalized by coating with Parylene C, a thin insulator with a high dielectric constant, coupled with selective re-exposure of the carbon surface using laser ablation.

MAIN RESULTS

We demonstrate the use of this technology by implanting 16 channel arrays in the rat nucleus accumbens. Chronic electrophysiology and dopamine signals were detected 1 month post implant. Additionally, electrodes were left in the tissue, sliced in place during histology, and showed minimal tissue damage.

SIGNIFICANCE

Our results validate our new technology and methods, which will enable a more comprehensive circuit level understanding of the brain.

摘要

目的

在神经元水平上进行多模态测量可以深入了解局部回路功能。然而,大多数行为学研究都集中在一个或两个模态上,并且通常受到现有技术的限制。

方法

在这里,我们展示了一种电生理学记录、化学感应以及组织内电极尖端的组织学定位相结合的方法。关键的使能技术是底层碳纤维电极的使用,碳纤维电极体积小、导电且对多巴胺敏感。碳纤维通过聚对二甲苯 C 涂层进行功能化,聚对二甲苯 C 是一种具有高介电常数的薄绝缘体,并结合激光烧蚀选择性重新暴露碳纤维表面。

主要结果

我们通过在大鼠伏隔核中植入 16 通道阵列来演示该技术的应用。植入后 1 个月检测到慢性电生理学和多巴胺信号。此外,电极留在组织中,在组织学切片时原位切片,显示出最小的组织损伤。

意义

我们的结果验证了我们的新技术和方法,这将使我们能够更全面地了解大脑的回路水平。

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