In Vivo and In Vitro Electrochemical Impedance Spectroscopy of Acute and Chronic Intracranial Electrodes

IF 2.2 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Pub Date : 2024-06-06 DOI:10.3390/data9060078
K. P. O'Sullivan, B. Philip, Jonathan L. Baker, John D. Rolston, M. Orazem, Kevin J. Otto, Christopher R Butson
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Abstract

Invasive intracranial electrodes are used in both clinical and research applications for recording and stimulation of brain tissue, providing essential data in acute and chronic contexts. The impedance characteristics of the electrode–tissue interface (ETI) evolve over time and can change dramatically relative to pre-implantation baseline. Understanding how ETI properties contribute to the recording and stimulation characteristics of an electrode can provide valuable insights for users who often do not have access to complex impedance characterizations of their devices. In contrast to the typical method of characterizing electrical impedance at a single frequency, we demonstrate a method for using electrochemical impedance spectroscopy (EIS) to investigate complex characteristics of the ETI of several commonly used acute and chronic electrodes. We also describe precise modeling strategies for verifying the accuracy of our instrumentation and understanding device–solution interactions, both in vivo and in vitro. Included with this publication is a dataset containing both in vitro and in vivo device characterizations, as well as some examples of modeling and error structure analysis results. These data can be used for more detailed interpretation of neural recordings performed on common electrode types, providing a more complete picture of their properties than is often available to users.
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急性和慢性颅内电极的体内和体外电化学阻抗谱分析
侵入性颅内电极在临床和研究中都被用于记录和刺激脑组织,为急性和慢性病提供重要数据。电极-组织界面(ETI)的阻抗特性会随着时间的推移而发生变化,而且相对于植入前的基线会有显著变化。了解 ETI 特性如何对电极的记录和刺激特性产生影响,可以为通常无法获得其设备的复杂阻抗特性的用户提供有价值的见解。与在单一频率下表征电阻抗的典型方法不同,我们展示了一种使用电化学阻抗谱(EIS)来研究几种常用急慢性电极的 ETI 复杂特性的方法。我们还介绍了精确的建模策略,以验证我们仪器的准确性,并了解体内和体外设备与溶液之间的相互作用。本出版物还包括一个数据集,其中包含体外和体内设备表征,以及建模和误差结构分析结果的一些示例。这些数据可用于更详细地解释在常见电极类型上进行的神经记录,为用户提供更全面的电极特性。
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来源期刊
Data
Data Decision Sciences-Information Systems and Management
CiteScore
4.30
自引率
3.80%
发文量
0
审稿时长
10 weeks
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